Subjective and Objective Quality Assessment of Single-Channel Speech Separation Algorithms

Provided by: Institute of Electrical & Electronic Engineers
Topic: Software
Format: PDF
Previous studies on performance evaluation of Single-Channel Speech Separation (SCSS) algorithms mostly focused on Automatic Speech Recognition (ASR) accuracy as their performance measure. Assessing the separated signals by different metrics other than this has the benefit that the results are expected to carry on to other applications beyond ASR. In this paper, in addition to conventional speech quality metrics (PESQ and SNR-loss), the authors also evaluate the separation systems output using different source separation metrics: Blind Source Separation EVALuation (BSS EVAL) and Perceptual Evaluation methods for Audio Source Separation (PEASS) measures. In their experiments, they apply these measures on the separated signals obtained by two well-known systems in the SCSS challenge to assess the objective and subjective quality of their output signals.

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