AMSP : Advanced Methods for Speech Processing

1. AMSP : Advanced Methods for Speech Processing An expression of Interest to set up a Network of Excellence in FP6 Prepared by members of COST-277 and colleagues Submitted by Marcos FAUNDEZ-ZANUY Presented here by Gérard CHOLLETchollet@tsi.enst.fr GET-ENST/CNRS-LTCIhttp://www.tsi.enst.fr/~chollet

2. Outline • Rationale of the proposition • Objectives • Approaches • Modeling • Recognition by synthesis • Robustness to environmental conditions • Evaluation paradigm • Excellence • Integration and structuring effect

3. Rationale for the NoE-AMSP • The areas of Automatic Speech Processing (recognition, synthesis, coding, language identification, speaker verification) should be better integrated • Better models of Speech Production and Perception • Investigate Nonlinear Speech Processing • Understanding, Semantic interpretation

4. Integrated platform for Automatic Speech Processing

5. Levels of representations

6. Features of Speech Models • Reflect auditory properties of human perception • Explain articulatory movements • Surpass the limitations of the source-filter model • Capture the dynamics of speech • Capable of natural speech restitution • Be discriminant for segmental information • Robust to noise and channel distortions • Adaptable to new speakers and new environments

7. Time – Frequency distributions • Short Time Fourier Transform • Non-linear frequency scale (PLP, WLP), mel-cepstrum • Wavelets, FAMlets • Bilinear distributions (Wigner-Ville, Choi-Williams,...) • Instantaneous frequency, Teager operator • Time – dependent representations (parametric and non parametric) • Vector quantisation • Matrix quantisation, non linear prediction

8. Time-dependent Spectral Models • Temporal Decomposition (B. Atal, 1983) • Vectorial Autoregressive models with detection of model ruptures (A. DeLima, Y. Grenier) • Segmental parameterisation using a time-dependent polynomial expansion (Y. Grenier)

9. Modeling of segmental units • Hidden Markov Model • Markov Fields • Bayesian Networks, Graphical Models OR • Production models • Synthesis (concatenative or rule based) with voice transformation AND / OR • Non linear predictor

10. Expected achievements in Speech Coding and Synthesis • Modeling the non-linearities in Speech Production and Perception will lead to more accurate and/or compact parametric representations. • Integrate segmental recognition and synthesis techniques in the coding loop to achieve bit rates as low as a few 100's bps with natural quality • Develop voice transformation techniques in order to : • Adapt segmental coders to new speakers, • Modify the characteristics of synthetic voices

11. Expected achievements inSpeech Synthesis • Self-excited nonlinear feedback oscillators will allow to better match synthetic and human voices. • Current concatenative techniques should be supplemented (or replaced) by (nonlinear) model based generative techniques to improve quality, naturalness, flexibility, training and adaptation. • Model-based voice mimicry controled by textual, phonetic and/or parametric input should not only improve synthesis but also coding, recognition and speaker characterisation.

12. Automatic Speech Recognition • Limitations of the HMM and hybrid HMM-ANN approaches • Keyword spotting (detection with SVM), noise robustness, adaptation • Large Vocabulary Speech Recognition (SIROCCO) http://perso.enst.fr/~sirocco/index-en.html • Markov Random Fields, Bayesian Networks and Graphical Models

13. Markov Random Fields Bayesian Networks and Graphical Models • Speech modelling with state constrained • Markov Random Field over Frequency bands • (Guillaume Gravier and Marc Sigelle) • http://perso.enst.fr/~ggravier/recherche.html#these • Comparative framework to study MRF, • Bayesian Networks and Graphical Models. • http://www.cs.berkeley.edu/~murphyk/Bayes/bayes.html

14. Recognition by Synthesis • If we could drive a synthesizer with meaningful units (phone sequences, words,...) to produce a speech signal that mimics the one to recognize, we may come close to transcription. • Analysis by Synthesis (which is in fact modeling) is a powerful tool in recognition and coding. • A trivial implementation is indexing a labelled speech memory

15. A L I S P Automatic Language Independent Speech Processing Automatic discovery of segmental units for speech coding, synthesis, recognition, language identification and speaker verification.

16. The robustness issue : • Mismatch between training and testing conditions • High Order Statistics are less sensitive to environment and transmission noise than autocorrelation • CMS, RASTA filtering • Independent Component Analysis • From Speaker Independent to Speaker Dependent recognition (Personalisation)

17. Expected achievements inAutomatic Speech Recognition • Dynamic nonlinear models should allow to merge feature extraction and classification under a common paradigm • Such models should be more robust to noise, channel distortions and missing data (transmission errors and packet losses) • Indexing a speech memory may help in the verification of hypotheses (a technique shared with Very Low Bit Rate Coders) • Statistical language models should be supplemented with adapted semantic information (conceptual graphs)

18. Voice technology in Majordome • Server side background tasks: continuous speech recognition applied to voice messages upon reception • Detection of sender’s name and subject • User interaction: • Speaker identification and verification • Speech recognition (receiving user commands through voice interaction) • Text-to-speech synthesis (reading text summaries, E-mails or faxes)

19. Collaboration with COST-278 • COST-278: Vocal Dialogue is a continuation of COST-249 • High interest in Robust Speech Recognition, Word spotting, Speech to actions, Speaker adaptation,... • Some members contribute to the Eureka-MAJORDOME project • Could be the seed for a Network of Excellence in FP6

20. Evaluation paradigm • DARPA • NIST • http://www.nist.gov/speech/tests/spk/index.htm Could we organize evaluation campaigns in Europe ? The 6th program of the EU is trying to promote Networks of Excellence. How should excellence be evaluated ? Should financial support be correlated with evaluation results ?