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Speech recognition in MUMIS

Speech recognition in MUMIS. Mirjam Wester, Judith Kessens & Helmer Strik. Intro. Objective: Automatic speech recognition of football commentaries SPEX transcribed two matches for two languages (Dutch and English): England - Germany (Eng-Dld) and Yugoslavia -The Netherlands (Yug-Ned)

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Speech recognition in MUMIS

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  1. Speech recognition in MUMIS Mirjam Wester, Judith Kessens & Helmer Strik

  2. Intro • Objective: Automatic speech recognition of football commentaries • SPEX transcribed two matches for two languages (Dutch and English): • England - Germany (Eng-Dld) and • Yugoslavia -The Netherlands (Yug-Ned) • Commentaries and stadium noise are mixed

  3. Data Conversion • SPEX transcription: • text grid: • orthographic transcription • chunk alignment; chunk = a segment of speech of about 2 to 3 seconds • CD with one large wav file • Split according to chunk alignments

  4. Examples of data • Yug-Ned Dutch • Yug-Ned English • Eng-Dld Dutch • Eng-Dld English

  5. Statistics English matches have two commentators, Dutch only one. Overlapping segments have been disregarded.

  6. Training Dutch: • Yug-Ned ¾ of CD (19 min speech) • France Telecom Noise Reduction (FTNR) English: • Yug-Ned ¾ of CD (28 min speech) • FTNR For more information on France Telecom Noise Reduction tool see: B. Noé, J. Sienel, D. Jouvet, L. Mauuary, L. Boves, J. de Veth & F. de Wet “Noise Reduction for Noise Robust Feature Extraction for Distributed Speech Recognition”. In Proc. of Eurospeech ’01

  7. Test Dutch: • Yug-Ned ¼ of CD • 626 chunks, 1577 words • lexicon and language model based on complete Yug-Ned match English: • Yug-Ned ¼ of CD • 636 chunks, 2641 words • lexicon and language model based on complete Yug-Ned match

  8. SNR before and after FTNR tool

  9. WER results for Yug-Ned before and after FTNR

  10. Dutch – Polyphone • Data is phonetically rich sentences • Phone models were trained on: • Polyphone all speakers • Polyphone male speakers • Polyphone male speakers + MUMIS noise • Polyphone as bootstrap for segmentation of MUMIS material

  11. Polyphone models (Dutch)Yug-Ned test set

  12. Cross tests (Dutch & English) Cross-tests: • train on ¾ Yug-Ned test on ¼ Eng-Dld • train on ¾ Eng-Dld test on ¼ Yug-Ned

  13. MUMIS models (Dutch) Yug-Ned test Eng-Dld test

  14. MUMIS models (English) Yug-Ned test Eng-Dld test

  15. MUMIS models (Dutch+English) Yug-Ned test Eng-Dld test

  16. Function words vs content words word type Dutch data English data

  17. SNR vs. WER (1)

  18. SNR vs. WER (2)

  19. Discussion • WERs are high • Noise? • FTNR leads to lower SNR, but WERs do not improve substantially • Not enough training data? • Polyphone for training/bootstrapping does not lead to lower WERs than training on MUMIS data • Noisifying Polyphone with MUMIS gives encouraging results

  20. Discussion continued • Function words comprise ± 50% of the data, and cause great deal of the errors • Names are recognized very well • Function words not necessary for information extraction (?)

  21. Future work • Steps to noise robust speech recognition: • model/speaker adaptation • combinations of noisified Polyphone models and FTNR • Other issues: • transcription of more data • English, Dutch and German • preference specific games? radio? TV? • generic football specific language model • confidence measures?

  22. Future work continued Questions: • What type of output from ASR is needed? • word-graph • n-best list • top of the list • word spotting? only content words? • For research purposes: is it possible to obtain data that has not been mixed (noise + commentary)?

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