Optimal Near-End Speech Intelligibility Improvement Using CLPSO-Based Voice Transformation in Realistic Noisy Environments

The proposed work attempts to improve the near-end intelligibility of speech at very low signal-to-noise ratios (SNRs). Additionally, the prerequisite of noise statistics that existing intelligibility improvement methods require is not a limitation of the proposed approach. To this end, the shaping...

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Bibliographic Details
Published inCircuits, systems, and signal processing Vol. 41; no. 12; pp. 6999 - 7034
Main Authors Biswas, Ritujoy, Nathwani, Karan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2022
Springer Nature B.V
Subjects
Algorithms
Circuits and Systems
Cost function
Electrical Engineering
Electronics and Microelectronics
Engineering
Gaussian process
Instrumentation
Intelligibility
Optimization
Parameter modification
Particle swarm optimization
Signal processing
Signal quality
Signal,Image and Speech Processing
Speech
Speeches
CLPSO
SDR
STOI
PESQ
Speech intelligibility
Online AccessGet full text
ISSN0278-081X
1531-5878
DOI10.1007/s00034-022-02106-3

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Summary:The proposed work attempts to improve the near-end intelligibility of speech at very low signal-to-noise ratios (SNRs). Additionally, the prerequisite of noise statistics that existing intelligibility improvement methods require is not a limitation of the proposed approach. To this end, the shaping parameters of the voice transformation function (VTF) are optimized. This optimization of the shaping parameters of the VTF corresponds to the combined modification that includes formant shifting, nonuniform time scaling, smoothing, and energy re-distributions in comprehensive learning particle swarm optimization (CLPSO) framework. The optimal parameters of the combined modifications are obtained by jointly maximizing the short time objective intelligibility, perceptual evaluation of speech quality and signal-to-distortion ratio metrics being used as the cost function in CLPSO. The outcome at the end is an improvement in intelligibility that is significantly higher than the ones obtained by applying these methods individually, while preserving the quality. As a side result, a Gaussian process regression is also employed to estimate the shaping parameters of VTF at arbitrary SNRs—other than the ones which were used during CLPSO training.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-022-02106-3