An energy-constrained signal subspace method for speech enhancement and recognition in colored noise

Abstract

An energy-constrained signal subspace (ECSS) method is proposed for speech enhancement and recognition under an additive colored noise condition. The key idea is to match the short-time energy of the enhanced speech signal to the unbiased estimate of the short-time energy of the clean speech, which is proven very effective for improving the estimation of the noise-like, low-energy segments in the speech signal. The colored noise is modelled by an autoregressive (AR) process. A modified covariance method is used to estimate the AR parameters of the colored noise and a prewhitening filter is constructed based on the estimated parameters. The performance of the proposed algorithm was evaluated using the TI46 digit database and the TIMIT continuous speech database. It was found that the ECSS method can significantly improve the signal-to-noise ratio (SNR) and word recognition accuracy (WRA) for isolated digits and continuous speech under various SNR conditions.