Exemplar-based speech enhancement and its application to noise-robust automatic speech recognition

In this work an exemplar-based technique for speech enhancement of noisy speech is proposed. The technique works by finding a sparse representation of the noisy speech in a dictionary containing both speech and noise exemplars, and uses the activated dictionary atoms to create a time-varying filter to enhance the noisy speech. The speech enhancement algorithm is evaluated using measured signal to noise ratio (SNR) improvements as well as by using automatic speech recognition. Experiments on the PASCAL CHiME challenge corpus, which contains speech corrupted by both reverberation and authentic living room noise at varying SNRs ranging from 9 to -6 dB, confirm the validity of the proposed technique. Examples of enhanced signals are available at http://www.cs.tut.fi/ ̃ tuomasv/.