A Family of Stereo-Based Stochastic Mapping Algorithms for Noisy Speech Recognition

The performance of speech recognition systems degrades significantly when they are operated in noisy conditions. For example, the automatic speech recognition (ASR) frontend of a speech-to-speech (S2S) translation prototype that is currently developed at IBM [11] shows noticeable increase in its word error rate (WER) when it is operated in real field noise. Thus, adding noise robustness to speech recognition systems is important, especially when they are deployed in real world conditions. Due to this practical importance noise robustness has become an active research area in speech recognition. Interesting reviews that cover a wide variety of techniques can be found in [12], [18], [19]. Noise robustness algorithms come in different flavors. Some techniques modify the features to make them more resistant to additive noise compared to traditional front-ends. These novel features include, for example, sub-band based processing [4] and time-frequency distributions [29]. Other algorithms adapt the model parameters to better match the noisy speech. These include generic adaptation algorithms like MLLR [20] or robustness techniques as model-based VTS [21] and parallel model combination (PMC) [9]. Yet other methods design transformations that map the noisy speech into a clean-like representation that is more suitable for decoding using clean speech models. These are usually referred to as feature compensation algorithms. Examples of feature compensation algorithms include general linear space transformations [5], [30], the vector Taylor series approach [26], and ALGONQUIN [8]. Also a very simple and popular technique for noise robustness is multistyle training (MST)[24]. In MST the models are trained by pooling clean data and noisy data that resembles the expected operating environment. Typically, MST improves the performance of ASR systems in noisy conditions. Even in this case, feature compensation can be applied in tandem with MST during both training and decoding. It usually results in better overall performance compared to MST alone. This combination of feature compensation and MST is often referred to as adaptive training [22]. In this chapter we introduce a family of feature compensation algorithms. The proposed transformations are built using stereo data, i.e. data that consists of simultaneous recordings of both the clean and noisy speech. The use of stereo data to build feature mappings was very popular in earlier noise robustness research. These include a family of cepstral O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg