A Supervised Learning Approach to Monaural Segregation of Reverberant Speech

Abstract

Abstract-A major source of signal degradation in real environments is room reverberation. Monaural speech segregation in reverberant environments is a particularly challenging problem. Although inverse filtering has been proposed to partially restore the harmonicity of reverberant speech before segregation, this approach is sensitive to specific source/receiver and room configurations. This paper proposes a supervised learning approach to monaural segregation of reverberant voiced speech, which learns to map from a set of pitch-based auditory features to a grouping cue encoding the posterior probability of a time-frequency (T-F) unit being target dominant given observed features. We devise a novel objective function for the learning process, which directly relates to the goal of maximizing signal-to-noise ratio. The models trained using this objective function yield significantly better T-F unit labeling. A segmentation and grouping framework is utilized to form reliable segments under reverberant conditions and organize them into streams. Systematic evaluations show that our approach produces very promising results under various reverberant conditions and generalizes well to new utterances and new speakers.

Localization based stereo speech source separation using probabilistic time-frequency masking and deep neural networks

“…They used a discriminative training criterion for the neural networks to further enhance the monaural source separation performance. In [29], Jin and Wang proposed a supervised learning approach to monaural segregation of reverberant speech using the multiresolution cochleagram (MRCG) features [30].…”

“…Compared with the monaural segregation of reverberant speech in [29], the stereo speech separation in [31] tends to be more robust due to the use of spatial information. In [7], GMM is used to model the MV and IPD/ILD cues that contain spatial information and the EM algorithm is used to estimate the model parameters and to derive the T-F mask.…”

Deep Q-network-based noise suppression for robust speech recognition

“…These algorithms successfully suppress the background noise, which is an inevitable problem in real world scenarios. Although speech enhancement techniques are found to be very helpful in tasks such as speech recognition, recognition performance is severely degraded in noisy conditions [3,4]. It is hence crucial that the speech recognition exhibits a robust performance across ambient noises and benefit many speech technology applications.…”

“…It is hence crucial that the speech recognition exhibits a robust performance across ambient noises and benefit many speech technology applications. In [4], the supervised learning approach is proposed to map a set of harmonic features into a pitch based grouping cue for time-frequency (T-F) unit.…”