IMPROVING RECOGNITION PERFORMANCE IN NOISY

ENVIRONMENTS• Joseph Picone1

Inst. for Signal and Info. Processing Dept. Electrical and Computer Eng. Mississippi State University

• Contact Information:

Box 9571 Mississippi State University Mississippi State. Mississippi 39762

Tel: 662-325-3149 Fax: 662-325-2298 Email: picone@isip.msstate.edu

1. Three-time workshop survivor (’97-’99)!

CLSP SUMMER PLANNING WORKSHOP

OVERVIEWAURORA LVCSR EVALUATION

•WSJ 5K (closed task) with seven (digitally-added) noise conditions

•Common ASR system•Two participants:

QIO: QualC., ICSI, OGI; MFA: Moto., FrTel., Alcatel

•Client/server applications

•Evaluate robustness in noisy environments

•Propose a standard for LVCSR applications

Performance Summary

SiteTest Set

CleanNoise(Sennh)

Noise(MultiM)

Base (TS1)

15% 59% 75%

Base (TS2)

19% 33% 50%

QIO (TS2) 17% 26% 41%

MFA (TS2)

15% 26% 40%

STATE OF THE ARTADAPTIVE SIGNAL PROCESSING

•Commercial front ends use adaptive noise compensation:

•Advanced front ends use a variety of techniques including subspace methods, normalization, and multiple time scales:

•Aurora LVCSR eval did not address acoustic modeling issues and speaker/channel adaptation (by design).

PROPOSAL SUMMARY

•Focus on Aurora task (TS2):– multiple microphones; representative noise conditions– adaptation/multipass processing within a single utterance– establish benchmarks prior to workshop (incl. adaptation)

SIGNAL PROCESSING VS. ACOUSTIC MODELS

•Some possible themes:– knowledge vs. statistics– phone-dependent spectral models of speech and noise– multi-time scale analysis– subspace methods to separate speech and noise– iterative refinement

•Parallel research tracks:– noise robust front end processing– phone/state-specific features and/or noise models

• J. Picone, "Improving Speech Recognition Performance in Noisy Environments,” Mississippi State University, November 8, 2002 (http://www.isip.msstate.edu/publications/seminars/2002/clsp_pm/).

• N. Parihar and J. Picone, “DSR Front End LVCSR Evaluation – Baseline Recognition System Description,” Aurora Working Group, European Telecommunications Standards Institute, November 1, 2001 (http://www.isip.msstate.edu/publications/reports/aurora_frontend/2001).

• D. Machola, et al, “Evaluation of a Noise-Robust DSR Front End on Aurora Databases,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 17-20, September 2002.

• A. Adamia, et al, “Qualcomm-ICSI-OGI Features For ASR,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 21-24, September 2002.

• C.P. Chen, et al, “Front End Post-Processing and Back End Model Enhancement on the Aurora 2.0/3.0 Databases,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 241-244, September 2002.

• P. Mot´ý¡cek and L. Burget, “Noise Estimation For Efficient Speech Enhancement and Robust Speech Recognition,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 1033-1036, September 2002.

• J. Chen, et al, “Recognition of Noisy Speech Using Normalized Moments,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 2441-2444, September 2002.

• J. Wu and Q. Huo, “An Environment Compensated Minimum Classification Error Training Approach and Its Evaluation in Aurora 2 Database,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 453-456, September 2002.

• G. Saon and J.M. Huerta, “Improvements to the IBM Aurora 2 Multi-Condition System,” International Conference on Spoken Language Processing, Denver, Colorado, USA, pp. 469-4472, September 2002.

REFERENCESAURORA AND ICSLP’2002