Texture analysis approach for improving HMM speech recognition in presence of microinterruptions

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Texture analysis approach for improving HMM speech recognition in presence of microinterruptions

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A simple yet powerful algorithm for enhancing a signal corrupted by microinterruptions is outlined. The algorithm performs a texture analysis of the peak-based spectrogram image for correcting the damage caused by noise and has been used as a pre-processor in hidden Markov model (HMM) speech recognition. Improvements in accuracy as high as 16% have been obtained with the T120 database.

Inspec keywords: speech recognition; signal representation; hidden Markov models

Other keywords: microinterruptions; HMM speech recognition; texture analysis approach; hidden Markov model; T120 database; signal enhancement; peak-based spectrogram image

Subjects: Signal processing theory; Markov processes; Signal detection; Speech recognition and synthesis; Markov processes; Speech recognition

References

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      • R.M. Haralick . Statistical and structural approaches to texture. Proc. IEEE , 5 , 786 - 804
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      • R.J. McAulay , T.F. Quatieri . Speech analysis/synthesis based on a sinusoidal representation. IEEE Trans. , 4 , 744 - 754
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