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HIWIRE MEETING Trento, January 11-12, 2007

HIWIRE MEETING Trento, January 11-12, 2007. José C. Segura, Javier Ramírez. Schedule. PEQ HAFE IS07 setup New improvements in robust VAD Revised multiple observation LRT (MO-LRT) Improve noise reduction and frame-dropping. PEQ. Evaluation AURORA2, AURORA3, AURORA4 Compared to HEQ

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HIWIRE MEETING Trento, January 11-12, 2007

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  1. HIWIRE MEETINGTrento, January 11-12, 2007 José C. Segura, Javier Ramírez

  2. Schedule • PEQ • HAFE • IS07 setup • New improvements in robust VAD • Revised multiple observation LRT (MO-LRT) • Improve noise reduction and frame-dropping

  3. PEQ • Evaluation • AURORA2, AURORA3, AURORA4 • Compared to HEQ • PEQ shows better performance on all databases • Results using Loquendo recognizer • Improved results • Slight degradation on clean conditions

  4. PEQ / HEQ comparative results

  5. HAFE • In collaboration with TUC-NTUA • Released two C modules, integrated in HAFE V1.0 • Basic Analysis • VAD (LTSD) • Wiener filter (optional) • Output: WAV / MFCC / FB • Post-Processing • PEQ (optional) • Regression computation (optional) • Frame-Dropping (optional) • CMS /CMVN (optional)

  6. IS07 setup • Prepared an HTK setup for evaluation on the HIWIRE database • Training scripts based on LORIA ones • Test scripts include MLLR adaptation with variable number of utterances • Baseline results • Only for clean data • With and without adaptation

  7. IS07 setup (without adaptation)

  8. IS07 (with adaptation)

  9. A review of MO-LRT VAD • Multiple observation likelihood ratio test: • Given 2N+1 independent observations of the noisy speech • Hypothesis test: • G0 : All the observations in the buffer are non-speech • G1 : “ “ “ noisy speech • Gaussian model: where

  10. Hangover analysis

  11. Hangover analysis

  12. Revised MO-LRT • Given 2N+1 independent observations of the noisy speech: • All the possible hypothesis on the individual observations: hk= 0 : xk = n hk= 1 : xk = s + n • Hypothesis subsets

  13. Revised MO-LRT • We assume that just a single speech to non-speech or non-speech to speech transition can occur in h

  14. Compared to Sohn et al. VAD.

  15. ROC curves in quiet noise conditions (stopped car and engine running) and close talking microphone.

  16. ROC curves in high noise conditions (high speed over a good road) and distant talking microphone.

  17. Presented at ICASSP 2007: • Javier Ramirez, José C. Segura, Juan M. Górriz, “Revised contextual LRT for voice activity detection”, ICASSP 2007. • Under review: • Javier Ramírez, José C. Segura, Juan M. Górriz and Luz García, “Improved Voice Activity Detection Using Contextual Multiple Hypothesis Testing for Robust Speech Recognition”, IEEE Transactions on Audio, Speech and Language Processing.

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