Bidirectional Long-Short Term Memory Network-based Estimation of Reliable Spectral Component Locations

Abstract

An accurate Ideal Binary Mask (IBM) estimate is essential for Missing Feature Theory (MFT)-based speaker identifica-tion, as incorrectly labelled spectral components (where a com-ponent is either reliable or unreliable) will degrade the perfor-mance of an Automatic Speaker Identification (ASI) system ad-versely in the presence of noise. In this work a Bidirectional Re-current Neural Network (BRNN) with Long-Short Term Mem-ory (LSTM) cells is proposed for improved IBM estimation. The proposed system had an average IBM estimate accuracy improvement of 4.5% and an average MFT-based speaker iden-tification accuracy improvement of 3.1% over all tested SNRdB levels, when compared to the previously proposed Multilayer Perceptron (MLP)-IBM estimator. When used for speech en-hancement the proposed system had an average MOS-LQO (ob-jective quality measure) improvement of 0.32 and an average QSTI (objective intelligibility measure) improvement of 0.01 over all tested SNRdB levels, when compared to the MLP-IBM estimator. The results presented in this work highlight the effec-tiveness of the proposed BRNN-IBM estimator for MFT-based speaker identification and IBM-based speech enhancement.