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Speech Enhancement Using Source Information for Phoneme Recognition of Speech with Background Music

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Abstract

This work explores the significance of source information for speech enhancement resulting in better phoneme recognition of speech with background music segments. Standard procedure for speech enhancement in noisy conditions involves sequential processing in terms of the temporal, spectral and perceptual methods. This work follows the same sequential processing but with the additional modification of studying the effect of source, particularly in the temporal and perceptual-based enhancement techniques for enhancing speech with background music segments. The source information is studied in terms of the epoch locations and epoch strength, obtained after passing the sum of the mean and standard deviation of the component envelopes computed across frequencies obtained using the single frequency filter (SFF), through a zero frequency filter (ZFF). This method of obtaining epoch locations and epoch strength will be termed as SFF-ZFF in this work. The enhanced segments are passed through a phoneme recognizer built using Gaussian mixture model-hidden Markov model (GMM-HMM), subspace Gaussian mixture model-hidden Markov model (SGMM-HMM) and deep neural network-hidden Markov model (DNN-HMM) system, where the models are trained on clean speech. The enhanced audio files show a better phone error rate than the degraded audio files, which means that performing enhancement in terms of the source information is significant for the speech with background music regions.

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Acknowledgements

This work is part of the project titled Multi-modal Broadcast Analytics: Structured Evidence Visualization for Events of Security Concern funded by the e-Security division of the Department of Electronics and Information Technology (DeitY), Govt. of India.

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Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    Banriskhem K. Khonglah

  2. Department of Electronics and Communication Engineering, Gauhati University Institute of Science and Technology (GUIST), Guwahati, 781014, India

    Abhishek Dey

  3. Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, 580011, India

    S. R. Mahadeva Prasanna

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  1. Banriskhem K. Khonglah
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  2. Abhishek Dey
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  3. S. R. Mahadeva Prasanna
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Correspondence to Banriskhem K. Khonglah.

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Khonglah, B.K., Dey, A. & Prasanna, S.R.M. Speech Enhancement Using Source Information for Phoneme Recognition of Speech with Background Music. Circuits Syst Signal Process 38, 643–663 (2019). https://doi.org/10.1007/s00034-018-0873-x

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