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Non-intrusive speech quality assessment using several combinations of auditory features

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Abstract

Quality estimation of speech is essential for monitoring and maintenance of the quality of service at different nodes of modern telecommunication networks. It is also required in the selection of codecs in speech communication systems. There is no requirement of the original clean speech signal as a reference in non-intrusive speech quality evaluation, and thus it is of importance in evaluating the quality of speech at any node of the communication network. In this paper, non-intrusive speech quality assessment of narrowband speech is done by Gaussian Mixture Model (GMM) training using several combinations of auditory perception and speech production features, which include principal components of Lyon’s auditory model features, MFCC, LSF and their first and second differences. Results are obtained and compared for several combinations of auditory features for three sets of databases. The results are also compared with ITU-T Recommendation P.563 for non-intrusive speech quality assessment. It is found that many combinations of these feature sets outperform the ITU-T P.563 Recommendation under the test conditions.

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Notes

  1. http://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat.

  2. http://www.utdallas.edu/~loizou/speech/noizeus.

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Acknowledgements

The authors would like to thank Mr. Yi Hu and Dr. Philipos C. Loizou of Department of Electrical Engineering, The University of Texas at Dallas, Richardson, TX75083-0688, USA for providing the NOIZEUS-2240 database of 2240 speech utterances passed through different speech processing algorithms at different noisy conditions to create the different degradations. The authors would also like to thank Prof. S.C. Saxena, Vice Chancellor (Acting), Jaypee Institute of Information Technology, Noida, India for providing the suitable environment to the corresponding author to complete the work.

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  1. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology (JIIT), Noida, India

    Rajesh Kumar Dubey

  2. CARE, Indian Institute of Technology (IIT), Delhi, India

    Arun Kumar

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  1. Rajesh Kumar Dubey
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  2. Arun Kumar
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Correspondence to Rajesh Kumar Dubey.

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Dubey, R.K., Kumar, A. Non-intrusive speech quality assessment using several combinations of auditory features. Int J Speech Technol 16, 89–101 (2013). https://doi.org/10.1007/s10772-012-9162-4

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