Towards Environment-Independent Spoken Language Systems

Towards Environment-Independent Spoken Language Systems Alejandro Acero and Richard M. Stern Department of Electrical and Computer Engineering School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract In this paper we discuss recent results from our efforts to make SPHINX, the CMU continuous-speech speaker- independent recognition system, robust to changes in the environment. To deal with differences in noise level and spectral tilt between close-talking and desk-top microphones, we describe two novel methods based on ad- ditive corrections in the cepstral domain. In the first algo- rithm, an additive correction is imposed that depends on the instantaneous SNR of the signal. In the second tech- nique, EM techniques are used to best match the cepstral vectors of the input utterances to the ensemble of codebook entries representing a standard acoustical ambience. Use of these algorithms dramatically improves recognition ac- curacy when the system is tested on a microphone other than the one on which it was trained. Introduction There are many sources of acoustical distortion that can degrade the accuracy of speech-recognition systems. For example, obstacles to robustness include additive noise from machinery, competing talkers, etc., reverberation from surface reflections in a room, and spectral shaping by microphones and the vocal tracts of individual speakers. These sources of distortion cluster into two complementary classes: additive noise (as in the first two examples) and distortions resulting from the convolution of the speech sig- nal with an unknown linear system (as in the remaining three). A number of algorithms for speech enhancement have been proposed in the literature. For example, Boll [3] and Beroufi et al. [2] introduced the spectral subtraction of DFT coefficients, and Porter and Boll [11] used MMSE techniques to estimate the DFT coefficients of corrupted speech. Spectral equalization to compensate for convolved distortions was introduced by Stockham et al. [13]. Recent applications of spectral subtraction and spectral equaliza- tion for speech recognition systems include the work of Van Compemolle [5] and Stem and Acero [12]. Although relatively successful, the above methtxls all depend on the assumption of independence of the spectral estimates across fr~uencies. Erell and Weimranb [6] demonstrated improved performance with an MMSE estimator in which correlation among frequencies is modeled explicitly. 157 Acero and Stem [1] proposed an approach to environment normalization in the cepstral domain, going beyond the noise stripping problem. In this paper we present two algorithms for speech nor- malization based on additive corrections in the cepstral domain and compare them to techniques that operate in the frequency domain. We have chosen the cepstral domain rather than the frequency domain so that can we work directly with the parameters that SPHINX uses, and because speech can be characterized with a smaller number of parameters in the cepstral domain than in the frequency domain. The first algorithm, SNR-dependent cepstral normalization (SDCN) is simple and effective, but it can- not be applied to new microphones without microphone- specific training. The second algorithm, codeword-dependent cepstral normalization (CDCN) uses the speech knowledge represented in a codebook …

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