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access icon free Performance improvement of monaural speech separation system using image analysis techniques

© The Institution of Engineering and Technology
Received 04/08/2017, Accepted 28/03/2018, Revised 22/03/2018, Published 06/04/2018

This research work proposes an image analysis-based algorithm to enhance the time–frequency (T–F) mask obtained in the initial segmentation of CASA-based monaural speech separation system to improve speech quality and intelligibility. It consists of labelling the initial segmentation mask, boundary extraction, active pixel detection and eliminating the non-active pixels related to noise. In labelling, the T–F mask obtained is labelled as periodicity pixel ( P ) matrix and non-periodicity pixel ( NP ) matrix. Next speech boundaries are created by connecting all the possible nearby P and NP matrix. Some speech boundary may include noisy T–F units as holes; these holes are treated using the proposed algorithm. The proposed algorithm is evaluated with the quality and intelligibility measures such as signal to noise ratio (SNR), perceptual evaluation of speech quality, , , coherence speech intelligibility index (CSII), normalised covariance metric (NCM), and short-time objective intelligibility (STOI). The experimental results show that the proposed algorithm improves the speech quality by increasing the SNR with an average value of 9.91 dB and reduces the by an average value of 25.6% and also improves the speech intelligibility in terms of CSII, NCM, and STOI when compared with the input noisy speech mixture

Inspec keywords: speech enhancement; source separation; edge detection; time-frequency analysis; feature extraction; speech intelligibility; image segmentation

Other keywords: nonactive pixels; periodicity pixel matrix; speech quality; short-time objective intelligibility; perceptual evaluation; time-frequency mask enhancement; speech intelligibility; coherence speech intelligibility index; CASA-based monaural speech separation system; T-F mask enhancement; P matrix; speech boundary; nonperiodicity pixel matrix; active pixel detection process; intelligibility measures; performance improvement; monaural speech separation system; boundary extraction; CSII; normalised covariance metric; NP matrix; signal to noise ratio; initial segmentation mask; image analysis techniques; STOI; NCM

Subjects: Speech processing techniques; Mathematical analysis; Mathematical analysis; Image recognition; Speech and audio signal processing; Computer vision and image processing techniques

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