hiwire meeting trento, january 11-12, 2007 josé c. segura, javier ramírez

HIWIRE MEETINGHIWIRE MEETINGTrento, January 11-12, 2007Trento, January 11-12, 2007

José C. Segura, Javier RamírezJosé C. Segura, Javier Ramírez

2 HIWIRE Meeting – Trento, 11 -12 January, 2007

Schedule

PEQ

HAFE

IS07 setup

New improvements in robust VAD Revised multiple observation LRT (MO-LRT) Improve noise reduction and frame-dropping

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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

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PEQ / HEQ comparative results

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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)

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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

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IS07 setup (without adaptation)

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IS07 (with adaptation)

0,00

5,00

10,00

15,00

20,00

25,00

0 2 5 10 20 50

Number of ADAP utterances

Err

or

(%)

WER

SER

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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

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Hangover analysis

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Hangover analysis

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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

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Revised MO-LRT

We assume that just a single speech to non-speech or non-speech to speech transition can occur in h

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Compared to Sohn et al. VAD.

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

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

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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.