Experiments with a nonlinear spectral subtractor (NSS), Hidden Markov models and the projection, for robust speech recognition in cars
| Autor: | Philip Lockwood, J. Boudy |
|---|---|
| Rok vydání: | 1992 |
| Předmět: |
Linguistics and Language
Computer science Communication Speech recognition Noise reduction Speech processing Markov model Language and Linguistics Computer Science Applications Speech enhancement Robustness (computer science) Modeling and Simulation Computer Vision and Pattern Recognition Hidden Markov model Software Utterance |
| Zdroj: | EUROSPEECH |
| ISSN: | 0167-6393 |
| DOI: | 10.1016/0167-6393(92)90016-z |
| Popis: | Achieving reliable performance for a speech recogniser is an important challenge, especially in the context of mobile telephony applications where the user can access telephone functions through voice. The breakthrough of such a technology is appealing, since the driver can concentrate completely and safely on his task while composing and conversing in a “full” hands-free mode. This paper addresses the problem of speaker-dependent discrete utterance recognition in noise. Special reference is made to the mismatch effects due to the fact that training and testing are made in different environments. A novel technique for noise compensation is proposed: nonlinear spectral subtraction (NSS). Robust variance estimates and robust pdf evaluations (projection) are also introduced and combined with NSS into the HMM framework. We show that the lower limit of applicability of the projection (low SNR values) can be loosened after combination with NSS. Experimental results are reported. The performance of an HMM-based recogniser rises from 56% (no compensation) to 98% after speech enhancement. More than 3300 utterances have been used to evaluate the systems (three databases, two European languages). This result is achieved by the use of robust training/recognition schemes and by preprocessing the noisy speech by NSS. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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