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Time-varying autoregressive tests for multiscale speech analysis

Published in:
INTERSPEECH 2009, 10th Annual Conf. of the International Speech Communication Association, pp. 2839-2842.

Summary

In this paper we develop hypothesis tests for speech waveform nonstationarity based on time-varying autoregressive models, and demonstrate their efficacy in speech analysis tasks at both segmental and sub-segmental scales. Key to the successful synthesis of these ideas is our employment of a generalized likelihood ratio testing framework tailored to autoregressive coefficient evolutions suitable for speech. After evaluating our framework on speech-like synthetic signals, we present preliminary results for two distinct analysis tasks using speech waveform data. At the segmental level, we develop an adaptive short-time segmentation scheme and evaluate it on whispered speech recordings, while at the sub-segmental level, we address the problem of detecting the glottal flow closed phase. Results show that our hypothesis testing framework can reliably detect changes in the vocal tract parameters across multiple scales, thereby underscoring its broad applicability to speech analysis.
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Summary

In this paper we develop hypothesis tests for speech waveform nonstationarity based on time-varying autoregressive models, and demonstrate their efficacy in speech analysis tasks at both segmental and sub-segmental scales. Key to the successful synthesis of these ideas is our employment of a generalized likelihood ratio testing framework tailored to...

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Adaptive short-time analysis-synthesis for speech enhancement

Published in:
2008 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 31 March - 4 April 2008.

Summary

In this paper we propose a multiresolution short-time analysis method for speech enhancement. It is well known that fixed resolution methods such as the traditional short-time Fourier transform do not generally match the time-frequency structure of the signal being analyzed resulting in poor estimates of the speech and noise spectra required for enhancement. This can lead to the reduction of quality in the enhanced signal through the introduction of artifacts such as musical noise. To counter these limitations, we propose an adaptive short-time analysis-synthesis scheme for speech enhancement in which the adaptation is based on a measure of local time-frequency concentration. Synthesis is made possible through a modified overlap-add procedure. Empirical results using voiced speech indicate a clear improvement over a fixed time-frequency resolution enhancement scheme both in terms of mean-square error and as indicated by informal listening tests.
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Summary

In this paper we propose a multiresolution short-time analysis method for speech enhancement. It is well known that fixed resolution methods such as the traditional short-time Fourier transform do not generally match the time-frequency structure of the signal being analyzed resulting in poor estimates of the speech and noise spectra...

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