A TAXONOMY-ORIENTED OVERVIEW OF NOISE COMPENSATION TECHNIQUES FOR SPEECH RECOGNITION

ABSTRACT Designing a machine that is capable for understanding human speech and responds properly to speech utterance or spoken language has intrigued speech research community for centuries. Among others, one of the fundamental problems to building speech recognition system is acoustic noise. The performance of speech recognition system significantly degrades in the presence of ambient noise. Background noise not only causes high level mismatch between training and testing conditions due to unseen environment but also decreases the discriminating ability of the acoustic model between speech utterances by increasing the associated uncertainty of speech. This paper presents a brief survey on different approaches to robust speech recognition. The objective of this review paper is to analyze the effect of noise on speech recognition, provide quantitative analysis of well-known noise compensation techniques used in the various approaches to robust speech recognition and present a taxonomy-oriented overview of noise compensation techniques

Effect of phase-sensitive environment model and higher order VTS on noisy speech feature enhancement

Model-based techniques for robust speech recognition often require the statistics of noisy speech. In this paper, we propose two modifications to obtain more accurate versions of the statistics of the combined HMM (starting from a clean speech and a noise model). Usually, the phase difference between speech and noise is neglected in the acoustic environment model. However, we show how a phase-sensitive environment model can be efficiently integrated in the context of Multi-Stream Model-Based Feature Enhancement and gives rise to more accurate covariance matrices for the noisy speech. Also, by expanding the Vector Taylor Series up to the second order term, an improved noisy speech mean can be obtained. Finally, we explain how the front-end clean speech model itself can be improved by a preprocessing of the training data. Recognition results on the Aurora4 database illustrate the effect on the noise robustness for each of these modifications. 1

Effect of phase-sensitive environment model and higher order VTS on noisy speech feature enhancement

Model-based techniques for robust speech recognition often require the statistics of noisy speech. In this paper, we propose two modifications to obtain more accurate versions of the statistics of the combined HMM (starting from a clean speech and a noise model). Usually, the phase difference between speech and noise is neglected in the acoustic environment model. However, we show how a phase-sensitive environment model can be efficiently integrated in the context of Multi-Stream Model-Based Feature Enhancement and gives rise to more accurate covariance matrices for the noisy speech. Also, by expanding the Vector Taylor Series up to the second order term, an improved noisy speech mean can be obtained. Finally, we explain how the front-end clean speech model itself can be improved by a preprocessing of the training data. Recognition results on the Aurora4 database illustrate the effect on the noise robustness for each of these modifications.Stouten V., Van hamme H., Wambacq P., ''Effect of phase-sensitive environment model and higher order VTS on noisy speech feature enhancement'', Proceedings IEEE international conference on acoustics, speech, and signal processing - ICASSP'2005, vol. I, pp. 433-436, March 18-23, 2005, Philadelphia, PA, USA.status: publishe