SAICSIT '10 Proceedings of the 2010 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
International Journal of Speech Technology
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Band-limited speech (speech for which parts of the spectrum are completely lost) is a major cause for accuracy degradation of automatic speech recognition (ASR) systems particularly when acoustic models have been trained with data with a different spectral range. In this paper, we present an extensive study of the problem of ASR of band-limited speech with full-bandwidth acoustic models. Our focus is mainly on band-limited feature compensation, covering even the case of time-varying band-limiting distortions, but we also compare this approach to more common model-side techniques (adaptation and retraining) and explore the combination of feature-based and model-side approaches. The feature compensation algorithms proposed are organized in a unified framework supported by a novel mathematical model of the impact of such distortions on Mel-frequency cepstral coefficient (MFCC) features. A crucial and novel contribution is the analysis made of the relative correlation of different elements in the MFCC feature vector for the cases of full-bandwidth and limited-bandwidth speech, which justifies an important modification in the feature compensation scheme. Furthermore, an intensive experimental analysis is provided. Experiments are conducted on real telephone channels, as well as artificial low-pass and bandpass filters applied over TIMIT data, and results are given for different experimental constraints and variations of the feature compensation method. Results for other well-known robustness approaches, such as cepstral mean normalization (CMN), model retraining, and model adaptation are also given for comparison. ASR performance with our approach is similar or even better than model adaptation, and we argue that in particular cases such as rapidly varying distortions, or limited computational or memory resources, feature compensation is more convenient. Furthermore, we show that feature-side and model-side approaches may be combined, outperforming any of those approache- - s alone.