Noisy Reverberant Environments Research Articles

Speech recognition based on HMM decomposition and composition method with a microphone array in noisy reverberant environments

AbstractHandling background noise or echo (reverberation) etc. is very important for having an automated robot etc. recognize remote speech in a real environment. As effective schemes for handling this problem, noise reducing schemes such as model adaptation schemes including HMM decomposition and composition or microphone array (beamformer) signal processing, spectral subtraction, etc. have been proposed. In particular, a model adaptation scheme is very effective for speech recognition in a noisy environment and its recognition performance increases in proportion to the signal‐to‐noise ratio (SNR). In this paper, improving the recognition performance in a low‐SNR environment by receiving speech at a high SNR using a microphone array before HMM decomposition and composition is attempted. The results of speech recognition experiments conducted in a noisy environment in an acoustic laboratory show an improvement in the recognition rate of about 25% by the proposed method for the case in which the SNR in a single microphone is 0 dB, as compared with the cases of using microphone array signal processing, HMM decomposition and composition alone. In addition, the proposed method shows recognition performance comparable to the case of using cepstrum mean normalization and spectral subtraction performed with an optimal coefficient given to the speech after microphone array processing. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 85(9): 13–22, 2002; Published online in Wiley InterScience (www.interscience. wiley.com). DOI 10.1002/ecjb.10068

Speech enhancement using sub-band adaptive Griffiths–Jim signal processing

Results are presented from intelligibility tests of a two-microphone sub-band adaptive Griffiths–Jim (SBAGJ) processing scheme that has possible application to future hearing aids as a method of improving speech intelligibility and quality in a noisy reverberant environment. This SBAGJ scheme combines sub-band processing with a Griffiths–Jim “front-end” that delivers a simple system more robust to the “causality” issue inherent with some multi-microphone adaptive noise cancelling configurations. Intelligibility testing is described and results presented of an assessment of the SBAGJ scheme using ten normal hearing listeners and signals from a real reverberant environment. The results support the hypothesis that speech processing using the SBAGJ scheme can provide a statistically and practically significant improvement in speech intelligibility. Analysis of mean opinion score data shows a corresponding statistically significant improvement in subjective quality.

Model adaptation by HMM decomposition and composition in noisy reverberant environments

Model adaptation by HMM decomposition and composition in noisy reverberant environments

A method to determine the speech transmission index from speech waveforms.

A method for computing the speech transmission index (STI) using real speech stimuli is presented and evaluated. The method reduces the effects of some of the artifacts that can be encountered when speech waveforms are used as probe stimuli. Speech-based STIs are computed for conversational and clearly articulated speech in several noisy, reverberant, and noisy-reverberant environments and compared with speech intelligibility scores. The results indicate that, for each speaking style, the speech-based STI values are monotonically related to intelligibility scores for the degraded speech conditions tested. Therefore, the STI can be computed using speech probe waveforms and the values of the resulting indices are as good predictors of intelligibility scores as those derived from MTFs by theoretical methods.