Frame-synchronous noise compensation for hands-free speech recognition in car environments

Jen-Tzung Chien*, M. S. Lin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

It has become increasingly important to develop hands-free speech recognition techniques for the human-computer interface in car environments. However, severe car noise degrades the speech recognition performance substantially. To compensate the performance loss, it is necessary to adapt the original speech hidden Markov models (HMMs) to meet changing car environments. A novel frame-synchronous adaptation mechanism for in-car speech recognition is presented. This mechanism is intended to perform unsupervised model adaptation efficiently on a frame-by-frame basis instead of a conventional adaptation algorithm relying on batch adaptation data and supervision information. The proposed adaptation scheme is performed during frame likelihood calculation where an optimal equalisation factor is first computed to equalise the model mean vector and the input frame vector. This equalisation factor then serves as a reference index to retrieve an additional bias vector for model mean adaptation. As a result, a rapid and flexible algorithm is exploited to establish a new robust likelihood measure. In experiments on hands-free in-car speech recognition with the microphone far from the talker, this framework is found to be effective in terms of recognition rate and computational cost under various driving speeds.

Original languageEnglish
Pages (from-to)508-515
Number of pages8
JournalIEE Proceedings: Vision, Image and Signal Processing
Volume147
Issue number6
DOIs
StatePublished - 1 Dec 2000

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