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Predicting automatic speech recognition performance over communication channels from instrumental speech quality and intelligibility scores

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Author: Gallardo, L.F. · Möller, S. · Beerends, J.
Type:article
Date:2017
Publisher: International Speech Communication Association
Source:Lacerda, F.Strombergsson, S.Wlodarczak, M.Heldner, M.Gustafson, J.House, D., 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017. 20 August 2017 through 24 August 2017, 2939-2943
Identifier: 782878
Keywords: Automatic speech recognition · Communication channels · Instrumental speech quality · Speech intelligibility · Communication channels (information theory) · Forecasting · Speech · Speech communication · Speech intelligibility · Speech transmission · Channel distortions · Instrumental measurements · Intelligibility scores · Recognition accuracy · Speech quality · Speech recognition performance · Transmission channels · Speech recognition · 2016 ICT · CSR - Cyber Security & Robustness · TS - Technical Sciences

Abstract

The performance of automatic speech recognition based on coded-decoded speech heavily depends on the quality of the transmitted signals, determined by channel impairments. This paper examines relationships between speech recognition performance and measurements of speech quality and intelligibility over transmission channels. Different to previous studies, the effects of super-wideband transmissions are analyzed and compared to those of wideband and narrowband channels. Furthermore, intelligibility scores, gathered by conducting a listening test based on logatomes, are also considered for the prediction of automatic speech recognition results. The modern instrumental measurement techniques POLQA and POLQA-based intelligibility have been respectively applied to estimate the quality and the intelligibility of transmitted speech. Based on our results, polynomial models are proposed that permit the prediction of speech recognition accuracy from the subjective and instrumental measures, involving a number of channel distortions in the three bandwidths. This approach can save the costs of performing automatic speech recognition experiments and can be seen as a first step towards a useful tool for communication channel designers. Copyright © 2017 ISCA. Amazon Alexa; Apple; DiDi; et al.; Furhat Robotics; Microsoft