HMM-based speech enhancement using harmonic modeling

This paper describes a technique for reduction of non-stationary noise in electronic voice communication systems. Removal of noise is needed in many such systems, particularly those deployed in harsh mobile or otherwise dynamic acoustic environments. The proposed method employs state-based statistical models of both speech and noise, and is thus capable of tracking variations in noise during sustained speech. This work extends the hidden Markov model (HMM) based minimum mean square error (MMSE) estimator to incorporate a ternary voicing state, and applies it to a harmonic representation of voiced speech. Noise reduction during voiced sounds is thereby improved. Performance is evaluated using speech and noise from standard databases. The extended algorithm is demonstrated to improve speech quality as measured by informal preference tests and objective measures, to preserve speech intelligibility as measured by informal Diagnostic Rhyme Tests, and to improve the performance of a low bit-rate speech coder and a speech recognition system when used as a pre-processor.