1. On the use of Intonation in ASR: preliminary results March, 13th 14th 2003 Meeting COST 275 Halmstad

2. OUR GROUP UPV EI Bilbao Inma Hernáez (leader), Eva Navas, Jon Sanchez UVA ETSII Valladolid Valentín Cardeñoso, Isaac Moro, Carlos Vivaracho, David Escudero. Involved in a CICYT Project of Biometrics with Javier Ortega (Madrid) and Marcos Faundez (Barcelona). Experience in ASR and in Modelling Intonation

3. OUR AIM To work in the CICYT Project with the rest of the groups. To apply our knowledge in Intonation to the field of ASR. Here we present our preliminary results.

4. INTRODUCTION Why to make use of intonation in ASR? It is a feature that characterize to the speaker: Speakers of the same group have a similar prosody. Each speaker can have its own prosody. It is a very robust feature Different sessions Different microphones Other experiences in applying intonation to ASR SUPER SID: very simple model of intonation.

5. INTRODUCTION Aim of this preliminary work To show the potential capabilities of intonation facing different sessions and microphones To show that it can be important to make use of “sophisticated” models for getting benefits in ASR. Overview Presentation of the model of intonation. The corpus. The experiment of speaker verification. Considerations about the robustness of the results. Consideration about the use model of intonation. Conclusions and future work.

6. Modelling Intonation

8. BASIC IDEA FOR ITS APPLICATION TO ASR: TO COMPARE THE MODELS OF DIFFERENT SPEAKERS

9. Modelling Intonation

10. The Corpus Recorded at EUPMT by Marcos Faundez One paragraph read by 16 speakers in 2 sessions with 3 microphones. Each paragraph = 11 sentences, 106 stress groups. 3816 intonation units. Speakers are male and in the same social group. The pitch was obtained automatically and segmented into intonation units by hand. Intonation was parameterised according to the intonation model.

11. The Experiment Speaker Verification. We have 6 recordings for each of the Speakers: 5 for modelling and 1 for testing. Each Speaker will have each Impostor. The impostor is modelled with the samples of the rest of speakers. We will repeat the experiment of verification six times (one for each of the possible set of tests) for each of the speakers. The classifier is based on Decision Trees C.45. Freeware WEKA.

12. Results Low rates, except for some of the speakers

13. Results: robustness No significant changes when different test input

14. Results: relevance of prosodic knowledge. Some parts of the utterance are more relevant depending of the speaker

15. Conclusions and future work Promising results: some speakers are recognised with high rates. Results are robust to changes in the session and in the microphones. Future work: To test the benefits of including this results in a ASR system. To explore the use of our methodology for modelling intonation in a more general way. Making use of more classes of intonation. Getting knowledge of which of the classes of intonation are more relevant for characterizing to the speaker. New corpura are welcome.

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