Microphone Arrays and Neural Networks for Robust Speech Recognition

vIicrophone Arrays and Neural Networks for Robust Speech Recognition C. Che +, Q. Lin +, J. Pearson*, B. de Vries*, and J. Flanagan + +CAIP Center, Rutgers University, Piscataway, NJ 08855-1390 and *David Sarnoff Research Center, Princeton, NJ 08543-5300 ABSTRACT This paper explores use of synergistically-integrated systems of microphone arrays and neural networks for robust speech recognition in variable acoustic environments, where the user must not be encumbered by microphone equipment. Existing speech recognizers work best for "high-quality close-talking speech." Performance of these recognizers is typically de- graded by environmental interference and mismatch in train- ing conditions and testing conditions. It is found that use of microphone arrays and neural network processors can elevate the recognition performance of existing speech recognizers in an adverse acoustic environment, thus avoiding the need to retrain the recognizer, a complex and tedious task. We also present results showing that a system of microphone arrays and neural networks can achieve a higher word recognition accuracy in an unmatched training/testing condition than that obtained with a retrained speech recognizer using array speech for both training and testing, i.e., a matched train- ing/testing condition. 1. INTRODUCTION Hidden Markov Models (HMM's) have to date been accepted as an effective classification method for large vocabulary con- tinuous speech recognition. Existing HMM-based recognition systems, e.g., SPHINX and DECIPHER, work best for "high- quality close-talking speech." They require consistency in sound capturing equipment and in acoustic environments be- tween training and testing sessions. When testing conditions differ from training conditions, performance of these recog- nizers is typically degraded if they are not retrained to cope with new environmental effects. Retraining of HMM-based recognizers is complex and time- consuming. It requires recollection of a large amount of speech data under corresponding conditions and reestimation of HMM's parameters. Particularly great time and effort are needed to retrain a recognizer which operates in a speaker- independent mode, which is the mode of greatest general in- terest. Room reverberation and ambient noise also degrade per- formance of speech recognizers. The degradation becomes more prominent as the microphone is positioned more dis- rant from the speaker, for instance, in a teleconferenc- ing application. Previous work has demonstrated that beamforming/matched-filter microphone arrays can provide higher signal-to-noise ratios than can conventional micro- phones used at distances (see, e.g., [1, 2]). Consequently, there is increasing interest in microphone arrays for hands- free operation of speech processing systems [3]-[7]. In this report, a system of microphone arrays and neural net- works is described which expands the power and advantages of existing ARPA speech recognizers to practical acoustic en- vironments where users need not be encumbered by hand- held or body-worn microphone systems. (Examples include Combat Information Centers, large group conferences, and mobile hands-busy eyes~busy maintenance tasks.) Another advantage of the system is that the speech recognizer need not be retrained for each particular application environment. Through neural network computing, the system learns and compensates for environmental interference. The neural net- work transforms speech-feature data (such as cepstrum co- efflcients) obtained …

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