Jesse Sampermans Research Project Presentation
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Jesse Sampermans Research Project Presentation
- 1. Continuous Speech Keyword Spotting In by Jesse Sampermans (502400)
- 3. Overview
- 5. Overview 1. Hypothesis 2. Historic Overview
- 6. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ
- 7. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception
- 8. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression
- 9. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement
- 10. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine
- 11. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics
- 12. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 13. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 14. 1.Overview Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 15. 1. Hypothesis
- 16. 1. Hypothesis “Is it possible, with today’s known technology, to automatically trigger a recording device with a random word in a sentence over a telephone line?”
- 17. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 18. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 19. Overview 2. Historic Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 21. 2. Historic Overview • Early Days (1700 - 1900)
- 22. 2. Historic Overview • Early Days (1700 - 1900) First artificial speech synthesizer
- 23. 2. Historic Overview • Early Days (1700 - 1900) First artificial speech synthesizer - late 1700’s: Russian professor Christian Kratzenstein
- 24. 2. Historic Overview • Early Days (1700 - 1900) First artificial speech synthesizer - late 1700’s: Russian professor Christian Kratzenstein - Resonant tube attached to pipe organ
- 25. 2. Historic Overview • Early Days (1700 - 1900) First artificial speech synthesizer Enhancement - late 1700’s: Russian professor Christian Kratzenstein - Resonant tube attached to pipe organ
- 26. 2. Historic Overview • Early Days (1700 - 1900) First artificial speech synthesizer Enhancement - late 1700’s: Russian professor - mid 1800’s: Charles Christian Kratzenstein Wheatstone - Resonant tube attached to pipe organ
- 27. 2. Historic Overview • Early Days (1700 - 1900) First artificial speech synthesizer Enhancement - late 1700’s: Russian professor - mid 1800’s: Charles Christian Kratzenstein Wheatstone - Resonant tube attached to pipe - Replace tubes with leather organ resonators
- 28. 2. Historic Overview Wheatstone Resonator
- 29. 2. Historic Overview • Early Days (1700 - 1900)
- 30. 2. Historic Overview • Early Days (1700 - 1900) 1881: Gramophone
- 31. 2. Historic Overview • Early Days (1700 - 1900) 1881: Gramophone Alexander Graham Bell
- 32. 2. Historic Overview • Early Days (1700 - 1900) 1881: Gramophone Alexander Graham Bell - Dictation purposes
- 33. 2. Historic Overview • Early Days (1700 - 1900)
- 34. 2. Historic Overview • Early Days (1700 - 1900) 1939 World Fair: VODER
- 35. 2. Historic Overview • Early Days (1700 - 1900) 1939 World Fair: VODER Homer Dudley
- 36. 2. Historic Overview • Early Days (1700 - 1900) 1939 World Fair: VODER Homer Dudley - Based on Wheatstone Resonator
- 37. 2. Historic Overview • Early Days (1700 - 1900) 1939 World Fair: VODER Homer Dudley - Based on Wheatstone Resonator - Electrical & Mechanical Parts
- 38. 2. Historic Overview • First Speech Recognizers (1950 - 1980)
- 39. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Vs.
- 40. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Vs. - Digit Recognition System based on speech formants
- 41. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Vs. - Digit Recognition System - 10 syllable recognizer based on speech formants
- 42. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Vs. - Digit Recognition System - 10 syllable recognizer based on speech formants - Dynamic Time Warping
- 43. 2. Historic Overview • First Speech Recognizers (1950 - 1980)
- 44. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s
- 45. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs.
- 46. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs. Office Automation
- 47. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs. Office Automation - Voice typewriter
- 48. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs. Office Automation - Voice typewriter - Trained databases
- 49. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs. Office Automation Telecom Automation - Voice typewriter - Trained databases
- 50. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs. Office Automation Telecom Automation - Voice typewriter - Keyword Spotting - Trained databases
- 51. 2. Historic Overview • First Speech Recognizers (1950 - 1980) Commercialization 1960’s Vs. Office Automation Telecom Automation - Voice typewriter - Keyword Spotting - Trained databases - Large Audience
- 53. 2. Historic Overview • Modern evolutions (1980 - ...)
- 54. 2. Historic Overview • Modern evolutions (1980 - ...) - Hidden Markov Models
- 55. 2. Historic Overview • Modern evolutions (1980 - ...) - Hidden Markov Models - CMU “Sphynx” = commercial success
- 56. 2. Historic Overview • Modern evolutions (1980 - ...) - Hidden Markov Models - CMU “Sphynx” = commercial success - DARPA (Defense Advances Research Projects Agency) investments
- 57. 2. Historic Overview • Modern evolutions (1980 - ...) - Hidden Markov Models - CMU “Sphynx” = commercial success - DARPA (Defense Advances Research Projects Agency) investments - Battle Management
- 58. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 59. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 60. 3. Human Speech Organ Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 61. 3. Human Speech Organ
- 62. 3. Human Speech Organ
- 63. 3. Human Speech Organ - Lungs: pump air
- 64. 3. Human Speech Organ - Lungs: pump air - Larynx (Vocal Folds)
- 65. 3. Human Speech Organ - Lungs: pump air - Larynx (Vocal Folds) - Articulators (Tongue, Lips, ...)
- 66. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 67. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 68. 4. Phonetics & Speech Perception Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 69. 4. Phonetics & Speech Perception
- 70. 4. Phonetics & Speech Perception • Phonetics
- 71. 4. Phonetics & Speech Perception • Phonetics
- 72. 4. Phonetics & Speech Perception • Phonetics - Smallest part of human speech
- 73. 4. Phonetics & Speech Perception • Phonetics - Smallest part of human speech - Originated in India around 2500 BC
- 74. 4. Phonetics & Speech Perception • Phonetics - Smallest part of human speech - Originated in India around 2500 BC - IPA (International Phonetic Alphabet)
- 75. 4. Phonetics & Speech Perception • Phonetics - Smallest part of human speech - Originated in India around 2500 BC - IPA (International Phonetic Alphabet) - 44 phonemes in American English
- 76. 4. Phonetics & Speech Perception
- 77. 4. Phonetics & Speech Perception • Speech Perception
- 78. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues
- 79. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms)
- 80. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms)
- 81. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms)
- 82. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation
- 83. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation Identifying boundaries between words (lexical) or phonemes (phonetic)
- 84. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation Identifying boundaries between words (lexical) or phonemes (phonetic) [k] in “kit” and “caught” and [i] in “kit” and “kick”
- 85. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation Identifying boundaries between words (lexical) or phonemes (phonetic) [k] in “kit” and “caught” and [i] in “kit” and “kick” - Categorical Perception
- 86. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation Identifying boundaries between words (lexical) or phonemes (phonetic) [k] in “kit” and “caught” and [i] in “kit” and “kick” - Categorical Perception Identifying words from different speakers
- 87. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation Identifying boundaries between words (lexical) or phonemes (phonetic) [k] in “kit” and “caught” and [i] in “kit” and “kick” - Categorical Perception Identifying words from different speakers Categorize phonemes in brain
- 88. 4. Phonetics & Speech Perception • Speech Perception - Acoustic Cues Voice Onset Time: Unaspirated plosives (near 0 ms) Aspirated plosives (> 30 ms) Voiced plosives (< 0 ms) - Speech Segmentation Identifying boundaries between words (lexical) or phonemes (phonetic) [k] in “kit” and “caught” and [i] in “kit” and “kick” - Categorical Perception Identifying words from different speakers Categorize phonemes in brain Only native speakers
- 89. 4. Phonetics & Speech Perception • Speech Perception
- 90. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech
- 91. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme
- 92. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture”
- 93. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture” Speed of speech
- 94. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture” Speed of speech Fast → Shorter vowels, less pronounced stops, bad articulation
- 95. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture” Speed of speech Fast → Shorter vowels, less pronounced stops, bad articulation Speaker identity
- 96. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture” Speed of speech Fast → Shorter vowels, less pronounced stops, bad articulation Speaker identity - Gender and age differences
- 97. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture” Speed of speech Fast → Shorter vowels, less pronounced stops, bad articulation Speaker identity - Gender and age differences - Vocal chord size and hormone levels
- 98. 4. Phonetics & Speech Perception • Speech Perception - Variations in speech Phonetic environment can alter the sound of a phoneme [o] in “Bob” and [u] in “vulture” Speed of speech Fast → Shorter vowels, less pronounced stops, bad articulation Speaker identity - Gender and age differences - Vocal chord size and hormone levels - Place of birth
- 99. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 100. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 101. 5. Telephone Speech Coding & Compression Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 102. 5. Telephone Speech Coding & Compression
- 103. 5. Telephone Speech Coding & Compression • Early days: Analog
- 104. 5. Telephone Speech Coding & Compression • Early days: Analog - Speech converted to control voltage in the phone
- 105. 5. Telephone Speech Coding & Compression • Early days: Analog - Speech converted to control voltage in the phone - Passed through copper lines → crosstalk
- 106. 5. Telephone Speech Coding & Compression • Early days: Analog - Speech converted to control voltage in the phone - Passed through copper lines → crosstalk • 1980’s - present day: Digital
- 107. 5. Telephone Speech Coding & Compression • Early days: Analog - Speech converted to control voltage in the phone - Passed through copper lines → crosstalk • 1980’s - present day: Digital - Main advantages: Longer distance / greater speed / less carrier noise
- 108. 5. Telephone Speech Coding & Compression • Early days: Analog - Speech converted to control voltage in the phone - Passed through copper lines → crosstalk • 1980’s - present day: Digital - Main advantages: Longer distance / greater speed / less carrier noise - Use of Optic Fiber lines → no crosstalk
- 109. 5. Telephone Speech Coding & Compression
- 110. 5. Telephone Speech Coding & Compression • Now: Mobile Phones
- 111. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech
- 112. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data
- 113. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz
- 114. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz - Compressed full-rate (13 kbit/s) or half-rate (6,5 kbit/s) with 8kHz SR
- 115. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz - Compressed full-rate (13 kbit/s) or half-rate (6,5 kbit/s) with 8kHz SR • Technique: Linear Predictive Coding (LPC)
- 116. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz - Compressed full-rate (13 kbit/s) or half-rate (6,5 kbit/s) with 8kHz SR • Technique: Linear Predictive Coding (LPC) - Formants (human resonance) are removed from speech
- 117. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz - Compressed full-rate (13 kbit/s) or half-rate (6,5 kbit/s) with 8kHz SR • Technique: Linear Predictive Coding (LPC) - Formants (human resonance) are removed from speech - What is left = sine wave → digitized with Fourier transform
- 118. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz - Compressed full-rate (13 kbit/s) or half-rate (6,5 kbit/s) with 8kHz SR • Technique: Linear Predictive Coding (LPC) - Formants (human resonance) are removed from speech - What is left = sine wave → digitized with Fourier transform - Formants are synthesized again in the receivers cellphone
- 119. 5. Telephone Speech Coding & Compression • Now: Mobile Phones - GSM: Speech - UMTS: Data - Frequency content of 3100 kHz - Compressed full-rate (13 kbit/s) or half-rate (6,5 kbit/s) with 8kHz SR • Technique: Linear Predictive Coding (LPC) - Formants (human resonance) are removed from speech - What is left = sine wave → digitized with Fourier transform - Formants are synthesized again in the receivers cellphone - Of great interest for speech recognition
- 120. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 121. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 122. 6. Speech Enhancement Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 124. 6. Speech Enhancement • Pre-Filtering
- 125. 6. Speech Enhancement • Pre-Filtering - Frequency based
- 126. 6. Speech Enhancement • Pre-Filtering - Frequency based - Filter banks
- 127. 6. Speech Enhancement • Pre-Filtering - Frequency based - Filter banks - Commonly know as an equalizer
- 128. 6. Speech Enhancement • Pre-Filtering - Frequency based - Filter banks - Commonly know as an equalizer - Used adaptively to suppress unwanted frequencies
- 129. 6. Speech Enhancement • Pre-Filtering - Frequency based - Filter banks - Commonly know as an equalizer - Used adaptively to suppress unwanted frequencies - Boost low-end lost due to telephone coding
- 130. 6. Speech Enhancement • Pre-Filtering - Frequency based - Filter banks - Commonly know as an equalizer - Used adaptively to suppress unwanted frequencies - Boost low-end lost due to telephone coding - Improve audibility
- 132. 6. Speech Enhancement • Noise-Filtering
- 133. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction
- 134. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective
- 135. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise
- 136. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused
- 137. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering
- 138. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener
- 139. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener - Uses Fourier transform to detect noise
- 140. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener - Uses Fourier transform to detect noise - Stationary (non-adaptive)
- 141. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener - Uses Fourier transform to detect noise - Stationary (non-adaptive) - Uses deconvolution to remove noise
- 142. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener - Uses Fourier transform to detect noise - Stationary (non-adaptive) - Uses deconvolution to remove noise - Signal Subspace approach
- 143. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener - Uses Fourier transform to detect noise - Stationary (non-adaptive) - Uses deconvolution to remove noise - Signal Subspace approach - Represents noise and original signal in “layers”
- 144. 6. Speech Enhancement • Noise-Filtering - Spectral Substraction - Simple and effective - Uses the amplitude of the noise - “Underwater” effect if overused - Wiener Filtering - Invented in 1940’s by Norbert Wiener - Uses Fourier transform to detect noise - Stationary (non-adaptive) - Uses deconvolution to remove noise - Signal Subspace approach - Represents noise and original signal in “layers” - Assigns vectors to high and low amplitudes
- 146. 6. Speech Enhancement • Spectral Restoration
- 147. 6. Speech Enhancement • Spectral Restoration - Fixes dropouts in the signal.
- 148. 6. Speech Enhancement • Spectral Restoration - Fixes dropouts in the signal. - Works on a small scale
- 149. 6. Speech Enhancement • Spectral Restoration - Fixes dropouts in the signal. - Works on a small scale - Adds filtered full band noise in the gaps
- 150. 6. Speech Enhancement • Spectral Restoration - Fixes dropouts in the signal. - Works on a small scale - Adds filtered full band noise in the gaps - Listener perceives the signal as whole
- 151. 6. Speech Enhancement • Spectral Restoration - Fixes dropouts in the signal. - Works on a small scale - Adds filtered full band noise in the gaps - Listener perceives the signal as whole - Bad results with SREs
- 152. 6. Speech Enhancement • Spectral Restoration - Fixes dropouts in the signal. - Works on a small scale - Adds filtered full band noise in the gaps - Listener perceives the signal as whole - Bad results with SREs - Most SREs can fill the gap in a different way
- 153. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 154. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 155. 7. Speech Recognition Engine Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 156. 7. Speech Recognition Engine
- 157. 7. Speech Recognition Engine • Dynamic Time Warping (DTW)
- 158. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days
- 159. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech
- 160. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed
- 161. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed - e.g. A video is played twice. One time fast and one time slow. A DTW based algorithm will see that it is the same video
- 162. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed - e.g. A video is played twice. One time fast and one time slow. A DTW based algorithm will see that it is the same video - Compares speech to a speech database
- 163. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed - e.g. A video is played twice. One time fast and one time slow. A DTW based algorithm will see that it is the same video - Compares speech to a speech database - Needs training most of the time
- 164. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed - e.g. A video is played twice. One time fast and one time slow. A DTW based algorithm will see that it is the same video - Compares speech to a speech database - Needs training most of the time - Does not use phonemes
- 165. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed - e.g. A video is played twice. One time fast and one time slow. A DTW based algorithm will see that it is the same video - Compares speech to a speech database - Needs training most of the time - Does not use phonemes - Uses interval-based vectors.
- 166. 7. Speech Recognition Engine • Dynamic Time Warping (DTW) - Mostly used in the early days - Fast & simple but not accurate with complex speech - Measures similarities in time and speed - e.g. A video is played twice. One time fast and one time slow. A DTW based algorithm will see that it is the same video - Compares speech to a speech database - Needs training most of the time - Does not use phonemes - Uses interval-based vectors. - Vector taken at the wrong time = bad representation
- 167. 7. Speech Recognition Engine
- 168. 7. Speech Recognition Engine • Statistically Based Speech Recognition
- 169. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models
- 170. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models - Heart and soul of statistically based SREs
- 171. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models - Heart and soul of statistically based SREs - Allows use by people with different accents / dialects
- 172. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models - Heart and soul of statistically based SREs - Allows use by people with different accents / dialects - Markov Model: “predict” the future by knowing the current state
- 173. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models - Heart and soul of statistically based SREs - Allows use by people with different accents / dialects - Markov Model: “predict” the future by knowing the current state - Hidden Markov model: “predict” the current state by knowing the future
- 174. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models - Heart and soul of statistically based SREs - Allows use by people with different accents / dialects - Markov Model: “predict” the future by knowing the current state - Hidden Markov model: “predict” the current state by knowing the future - Future = grammar file
- 175. 7. Speech Recognition Engine • Statistically Based Speech Recognition Hidden Markov Models - Heart and soul of statistically based SREs - Allows use by people with different accents / dialects - Markov Model: “predict” the future by knowing the current state - Hidden Markov model: “predict” the current state by knowing the future - Future = grammar file - Statistically rules out possibilities as the word progresses
- 176. 7. Speech Recognition Engine • Statistically Based Speech Recognition
- 177. 7. Speech Recognition Engine • Statistically Based Speech Recognition Acoustic Model
- 178. 7. Speech Recognition Engine • Statistically Based Speech Recognition Acoustic Model - Gathers statistical information for the HMM
- 179. 7. Speech Recognition Engine • Statistically Based Speech Recognition Acoustic Model - Gathers statistical information for the HMM - Does this by analyzing a speech corpus (read or continuous)
- 180. 7. Speech Recognition Engine • Statistically Based Speech Recognition Acoustic Model - Gathers statistical information for the HMM - Does this by analyzing a speech corpus (read or continuous) - Different corpus (language, gender, frequency range)
- 181. 7. Speech Recognition Engine • Statistically Based Speech Recognition Acoustic Model - Gathers statistical information for the HMM - Does this by analyzing a speech corpus (read or continuous) - Different corpus (language, gender, frequency range) - ISIP Switchboard corpus: 240h of speech, 500 talkers. Telephone quality
- 182. 7. Speech Recognition Engine • Statistically Based Speech Recognition
- 183. 7. Speech Recognition Engine • Statistically Based Speech Recognition Language Model
- 184. 7. Speech Recognition Engine • Statistically Based Speech Recognition Language Model - Tries to predict the next word
- 185. 7. Speech Recognition Engine • Statistically Based Speech Recognition Language Model - Tries to predict the next word - Uses a grammar file
- 186. 7. Speech Recognition Engine • Statistically Based Speech Recognition Language Model - Tries to predict the next word - Uses a grammar file - E.g. “Phone Steve Young; Phone Young; Phone Steve; Phone Young Steve”
- 187. 7. Speech Recognition Engine • Statistically Based Speech Recognition Language Model - Tries to predict the next word - Uses a grammar file - E.g. “Phone Steve Young; Phone Young; Phone Steve; Phone Young Steve” - Multiple can be combined to predict entire sentences
- 188. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 189. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 190. 8. Speech Analytics Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 191. 8. Speech Analytics
- 192. 8. Speech Analytics - Separate engine
- 193. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed
- 194. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed • Audio Mining
- 195. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed • Audio Mining - Analyzes audio as soon as it enters the signal
- 196. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed • Audio Mining - Analyzes audio as soon as it enters the signal - Useful with background noise
- 197. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed • Audio Mining - Analyzes audio as soon as it enters the signal - Useful with background noise - Matches source to a speech database
- 198. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed • Audio Mining - Analyzes audio as soon as it enters the signal - Useful with background noise - Matches source to a speech database e.g.: Emotion detection with customer services
- 199. 8. Speech Analytics - Separate engine - Analyze gender, age, identity and topic discussed • Audio Mining - Analyzes audio as soon as it enters the signal - Useful with background noise - Matches source to a speech database e.g.: Emotion detection with customer services Music recognition software (“Shazam”, “Soundhound”)
- 200. 8. Speech Analytics
- 201. 8. Speech Analytics • Keyword Spotting
- 202. 8. Speech Analytics • Keyword Spotting 2 kinds:
- 203. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word
- 204. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word - clearly enforced breaks
- 205. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word - clearly enforced breaks - non-spontaneous
- 206. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word - clearly enforced breaks - non-spontaneous - user knows he is talking to an SRE
- 207. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word - clearly enforced breaks - non-spontaneous - user knows he is talking to an SRE Unconstrained spotting
- 208. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word - clearly enforced breaks - non-spontaneous - user knows he is talking to an SRE Unconstrained spotting - continuous speech KWS
- 209. 8. Speech Analytics • Keyword Spotting 2 kinds: Isolated word - clearly enforced breaks - non-spontaneous - user knows he is talking to an SRE Unconstrained spotting - continuous speech KWS - difficult due to speech segmentation
- 210. 8. Speech Analytics • Keyword Spotting
- 211. 8. Speech Analytics • Keyword Spotting 2 methods
- 212. 8. Speech Analytics • Keyword Spotting 2 methods - filler method (garbage-method): entire string of speech is analyzed
- 213. 8. Speech Analytics • Keyword Spotting 2 methods - filler method (garbage-method): entire string of speech is analyzed excess words too (=garbage)
- 214. 8. Speech Analytics • Keyword Spotting 2 methods - filler method (garbage-method): entire string of speech is analyzed excess words too (=garbage) - sliding model: interval based analyzing
- 215. 8. Speech Analytics • Keyword Spotting 2 methods - filler method (garbage-method): entire string of speech is analyzed excess words too (=garbage) - sliding model: interval based analyzing uses Hidden Markov Models & grammar file
- 216. 8. Speech Analytics • Keyword Spotting 2 methods - filler method (garbage-method): entire string of speech is analyzed excess words too (=garbage) - sliding model: interval based analyzing uses Hidden Markov Models & grammar file resource intensive
- 217. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 218. Overview 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 219. 9.Overview Conclusion 1. Hypothesis 2. Historic Overview 3. Human Speech Organ 4. Phonetics & Speech Perception 5. Telephone Speech Coding & Compression 6. Speech Enhancement 7. Speech Recognition Engine 8. Speech Analytics 9. Conclusion
- 220. 9. Conclusion
- 221. 9. Conclusion “Is it possible, with today’s known technology, to automatically trigger a recording device with a random word in a sentence over a telephone line?”
- 222. 9. Conclusion “Is it possible, with today’s known technology, to automatically trigger a recording device with a random word in a sentence over a telephone line?” Answer:
- 223. 9. Conclusion “Is it possible, with today’s known technology, to automatically trigger a recording device with a random word in a sentence over a telephone line?” Answer: YES
- 224. 9. Conclusion
- 225. 9. Conclusion - Keyword spotting algorithm based on a statistically based SRE
- 226. 9. Conclusion - Keyword spotting algorithm based on a statistically based SRE - Appropriate acoustic model
- 227. 9. Conclusion - Keyword spotting algorithm based on a statistically based SRE - Appropriate acoustic model - ISIP Switchboard speech corpus: telephone compressed source
- 228. 9. Conclusion - Keyword spotting algorithm based on a statistically based SRE - Appropriate acoustic model - ISIP Switchboard speech corpus: telephone compressed source - Grammar file? → Maybe but will be big
- 229. 9. Conclusion - Keyword spotting algorithm based on a statistically based SRE - Appropriate acoustic model - ISIP Switchboard speech corpus: telephone compressed source - Grammar file? → Maybe but will be big - Normal speech corpus? → A lot of pre-filtering / might nog be successful
- 230. 9. Conclusion - Keyword spotting algorithm based on a statistically based SRE - Appropriate acoustic model - ISIP Switchboard speech corpus: telephone compressed source - Grammar file? → Maybe but will be big - Normal speech corpus? → A lot of pre-filtering / might nog be successful - LPC? → artifacts in output due to 2x LPC filtering
- 232. Q&A
Notas do Editor
- \n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- - Complicated research\n\n- Start with core of research\n- Research question\n
- \n
- \n
- \n
- \n
- - Ask opinion\n\n- Idea: CSI\n
- \n
- \n
- \n
- \n
- \n
- \n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Early days: focus on Voice Reproduction\n\n- Produce Vowels\n- Different tube size/shape -> Differen vowel\n\n
- - Reed: represents vocal folds\n- Resonator changed by hand: Different vowels\n
- - Major breakthrough\n\n- Originally used for dictation\n\n- Aimed at office-market\n
- - Major breakthrough\n\n- Originally used for dictation\n\n- Aimed at office-market\n
- - Major breakthrough\n\n- Originally used for dictation\n\n- Aimed at office-market\n
- - Major breakthrough\n\n- Originally used for dictation\n\n- Aimed at office-market\n
- - Oscillator or Noise as source\n\n- Frequency -> controlled by foot pedal\n- Hands -> control band pass filters\n\n- Idea -> Importance of signal spectrum for speech representation\n
- - Oscillator or Noise as source\n\n- Frequency -> controlled by foot pedal\n- Hands -> control band pass filters\n\n- Idea -> Importance of signal spectrum for speech representation\n
- - Oscillator or Noise as source\n\n- Frequency -> controlled by foot pedal\n- Hands -> control band pass filters\n\n- Idea -> Importance of signal spectrum for speech representation\n
- - Oscillator or Noise as source\n\n- Frequency -> controlled by foot pedal\n- Hands -> control band pass filters\n\n- Idea -> Importance of signal spectrum for speech representation\n
- - Oscillator or Noise as source\n\n- Frequency -> controlled by foot pedal\n- Hands -> control band pass filters\n\n- Idea -> Importance of signal spectrum for speech representation\n
- - Computer popularity\n\n- BELL: Digit: \nonly works separated\nused formant frequencies to detect and compare\n\n
- - Computer popularity\n\n- BELL: Digit: \nonly works separated\nused formant frequencies to detect and compare\n\n
- - Computer popularity\n\n- BELL: Digit: \nonly works separated\nused formant frequencies to detect and compare\n\n
- - Computer popularity\n\n- BELL: Digit: \nonly works separated\nused formant frequencies to detect and compare\n\n
- - Computer popularity\n\n- BELL: Digit: \nonly works separated\nused formant frequencies to detect and compare\n\n
- - Computer popularity\n\n- BELL: Digit: \nonly works separated\nused formant frequencies to detect and compare\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - 60&#x2019;s -> Commercialization\n\n- IBM: Office market\n\n- AT&T: prvsly Bell labs\nTelecom automation\nHelp desks\nKeyword spotting: natural\n\n
- - Rise of statistically base SRE\n\n- Andrej Markov\n\n- CMU: Used HMM\n\n- DARPA: USA gov organization\n\n
- - Rise of statistically base SRE\n\n- Andrej Markov\n\n- CMU: Used HMM\n\n- DARPA: USA gov organization\n\n
- - Rise of statistically base SRE\n\n- Andrej Markov\n\n- CMU: Used HMM\n\n- DARPA: USA gov organization\n\n
- - Rise of statistically base SRE\n\n- Andrej Markov\n\n- CMU: Used HMM\n\n- DARPA: USA gov organization\n\n
- - Rise of statistically base SRE\n\n- Andrej Markov\n\n- CMU: Used HMM\n\n- DARPA: USA gov organization\n\n
- - Rise of statistically base SRE\n\n- Andrej Markov\n\n- CMU: Used HMM\n\n- DARPA: USA gov organization\n\n
- \n
- \n
- \n
- \n
- \n
- \n
- - Lungs: Pump air = fuel\n\n- Larynx: houses vocal folds\n- Vocal folds: no muscle\n- Comp to Reed in clarinet\n- Closed: speech\n- Open: breathing\n- Gap?: big/ small\n\n- Articulators: Shaping / Syllables\n
- - Lungs: Pump air = fuel\n\n- Larynx: houses vocal folds\n- Vocal folds: no muscle\n- Comp to Reed in clarinet\n- Closed: speech\n- Open: breathing\n- Gap?: big/ small\n\n- Articulators: Shaping / Syllables\n
- - Lungs: Pump air = fuel\n\n- Larynx: houses vocal folds\n- Vocal folds: no muscle\n- Comp to Reed in clarinet\n- Closed: speech\n- Open: breathing\n- Gap?: big/ small\n\n- Articulators: Shaping / Syllables\n
- - Lungs: Pump air = fuel\n\n- Larynx: houses vocal folds\n- Vocal folds: no muscle\n- Comp to Reed in clarinet\n- Closed: speech\n- Open: breathing\n- Gap?: big/ small\n\n- Articulators: Shaping / Syllables\n
- \n
- \n
- \n
- \n
- \n
- \n
- - Lowest level of speech which still form a contrast between sounds\n\n- Noted in an IPA\n\n- Used in the acoustic model of an SRE\n\n\n\n
- - Lowest level of speech which still form a contrast between sounds\n\n- Noted in an IPA\n\n- Used in the acoustic model of an SRE\n\n\n\n
- - Lowest level of speech which still form a contrast between sounds\n\n- Noted in an IPA\n\n- Used in the acoustic model of an SRE\n\n\n\n
- - Lowest level of speech which still form a contrast between sounds\n\n- Noted in an IPA\n\n- Used in the acoustic model of an SRE\n\n\n\n
- - Lowest level of speech which still form a contrast between sounds\n\n- Noted in an IPA\n\n- Used in the acoustic model of an SRE\n\n\n\n
- - Lowest level of speech which still form a contrast between sounds\n\n- Noted in an IPA\n\n- Used in the acoustic model of an SRE\n\n\n\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - AC: Defines start & end points of phonemes (VOT)\nStudy articulation\n\nUnaspirated: regular speech\nAspirated: my Macboo[k] is broken\nVoiced: [G]od, [B]ob\n\n- SS: difficult. \n[k] is pronounced different \n&#x201C;How to recognize speech&#x201D;\n&#x201C;How to wreck a nice beach&#x201D;\n\n- CP: different pronunciations\n&#x201C;sheep&#x201D; and &#x201C;cheap&#x201D;\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- - Psychological or Phonetic\n\n- VCsize: Female & child = small\n Male = big = more air\n
- \n
- \n
- \n
- \n
- \n
- \n
- - Fully Analog\n- Manual switchers\n\n
- - Fully Analog\n- Manual switchers\n\n
- - Fully Analog\n- Manual switchers\n\n
- - Fully Analog\n- Manual switchers\n\n
- - Fully Analog\n- Manual switchers\n\n
- - Fully Analog\n- Manual switchers\n\n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- Cellphone: Dr. Martin Cooper\n\n- GSM: Global Systems for Mobile Communications\n- UMTS: Universal Mobile Telecommunications System\n\n- LPC: remove buzz from the voice\nSignals carried digitally\nLPC data added in receiving phone \n
- \n
- \n
- \n
- \n
- \n
- \n
- - FB: Filter telephone mid frequency\n
- - FB: Filter telephone mid frequency\n
- - FB: Filter telephone mid frequency\n
- - FB: Filter telephone mid frequency\n
- - FB: Filter telephone mid frequency\n
- - FB: Filter telephone mid frequency\n
- - FB: Filter telephone mid frequency\n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
- After filtering -> Noise filtering\n\n- 3 methods\n\n- SS: Used in Ozone\n- Wiener = Linear, non-adaptive\n- Signal Subspace = layers \n
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