Decoding speech in the presence of other sources

The statistical theory of speech recognition introduced several decades ago has brought about low word error rates for clean speech. However, it has been less successful in noisy conditions. Since extraneous acoustic sources are present in virtually all everyday speech communication conditions, the failure of the speech recognition model to take noise into account is perhaps the most serious obstacle to the application of ASR technology. Approaches to noise-robust speech recognition have traditionally taken one of two forms. One set of techniques attempts to estimate the noise and remove its effects from the target speech. While noise estimation can work in low-to-moderate levels of slowly varying noise, it fails completely in louder or more variable conditions. A second approach utilises noise models and attempts to decode speech taking into account their presence. Again, model-based techniques can work for simple noises, but they are computationally complex under realistic conditions and require models for all sources present in the signal. In this paper, we propose a statistical theory of speech recognition in the presence of other acoustic sources. Unlike earlier model-based approaches, our framework makes no assumptions about the noise background, although it can exploit such information if it is available. It does not require models for background sources, or an estimate of their number. The new approach extends statistical ASR by introducing a segregation model in addition to the conventional acoustic and language models. While the conventional statistical ASR problem is to find the most likely sequence o