https://telecombcn-dl.github.io/2017-dlsl/ Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN. The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.

1. [course site]
Day 3 Lecture 3
Speaker ID I
Javier Hernando

2. 2
Acknowledgments
Miquel India, Omid Ghahabi, Pooyan Safari
Ph.D. candidates

3. 3
Speech Recognition
Emotion - Happy
Gender - Woman
Age - Teengger
.
.
.

4. 4
Speaker ID as Biometrics

5. 5
Security

6. 6
Applications

7. 7
Modalities

8. 8
Tasks

9. 9
Features
Humans use different features to recognise the speaker:
● Pronunciation, diction, …
● Prosody, rhythm, speed, volume,…
● Acoustic aspects of the voice.
Desirable aspects for those features:
● Practical
○ To appear frequently and naturally during the speech
○ Easily measurable for the system
● Robust
○ Any change over time or by speaker’s health
○ Any change by different transmission characteristics or by background noise
● Secure
○ Hard to falsify
No feature has all those properties
Spectrum-derived features are the more used by now because of their
effectiveness

10. 10
HMMs and GMMs

11. 11
GMM-UBM Universal Background Model
MAP Adaptation

12. 12
Supervectors

13. 13
i-vectors
Joint Factor Analysis (JFA) model

14. 14
i-Vector dimension

15. 15
i-Vector Training
T is trained iteratively according to the GMM posteriors
Given T and the UBM, i-vectors are extracted for each speaker utterance

16. 16
i-vector Scoring

17. 17
SoA Speaker Recognition

18. 18
DL in Speaker Recognition

19. 19
DL Features

20. 20
BN Features
After M. Mclaren e al.,“Advances in deep neural network approaches to speaker recognition” ICASSP 2015.

21. Autoencoder

22. Denoising Autoencoder
Denoising autoencoder for cepstral domain
dereverberation.
● Transfrom noisy features of
reverberant speech to clean speech
features.
● Pre-Trainning with Deep Belief
Networks (DBN)
Zhang et al., Deep neural network-based bottleneck
feature and denoising autoencoder-based fro
distant-talking speaker identification, EURASSIP Journal
on Audio, Speech, and Music Processing (2015) 2015:12