Monaural Speech Segregation Based on Pitch Track Correction Using An Ensemble Kalman Filter

Abstract

We propose a novel method of pitch track correction that uses an ensemble Kalman filter to improve the performance of monau- ral speech segregation. The proposed method considers all reliable pitch streaks for pitch track correction, whereas the conventional segregation approach relies on only the longest streak in a given speech stream. In addition, unreliable pitch streaks are corrected with an ensemble Kalman filter that uses autocorrelation functions as noisy observations for the hidden true pitch values. Our proposed approach provides more accu- rate pitch estimation, thus improving speech segregation perfor- mance for various types of noises, in particular, colored noise. In speech segregation experiments on mixtures of speech and various competing noises, the proposed method demonstrated superior performance to the conventional approach.