A Non-negative Approach to Language Informed Speech Separation

The use of high level information in source separation algorithms can greatly constrain the problem and lead to improved results by limiting the solution space to semantically plausible results. The automatic speech recognition community has shown that the use of high level information in the form of language models is crucial to obtaining high quality recognition results. In this paper, we apply language models in the context of speech separation. Specifically, we use language models to constrain the recently proposed non-negative factorial hidden Markov model. We compare the proposed method to non-negative spectrogram factorization using standard source separation metrics and show improved results in all metrics.