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Multiband Excitation for Speech Enhancement

Werayuth Charoenruengkit, Nurgün Erdöl

Open publisher page 1 citations

Abstract

The speech enhancement algorithm proposed aims to improve the quality of denoised speech by introducing voicing information to a Wiener-type spectral amplitude gain function. A constrained multiband excitation (MBE) model is used to emphasize harmonic components of the glottal input; and a low variance and bias autoregressive multitaper (ARMT) estimate models the vocal tract. Objective and subjective evaluations show improvement over unconstrained models and those using high variance spectrum estimators.

About this research paper

What this paper is about

The speech enhancement algorithm proposed aims to improve the quality of denoised speech by introducing voicing information to a Wiener-type spectral amplitude gain function. A constrained multiband excitation (MBE) model is used to emphasize harmonic components of the glottal input; and a low variance and bias autoregressive multitaper (ARMT) estimate models the vocal tract. Objective and subjective evaluations show improvement over unconstrained models and those using high variance spectrum estimators.

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Available abstract

The speech enhancement algorithm proposed aims to improve the quality of denoised speech by introducing voicing information to a Wiener-type spectral amplitude gain function. A constrained multiband excitation (MBE) model is used to emphasize harmonic components of the glottal input; and a low variance and bias autoregressive multitaper (ARMT) estimate models the vocal tract. Objective and subjective evaluations show improvement over unconstrained models and those using high variance spectrum estimators.

Key concepts: Multitaper, Speech enhancement, Estimator, Vocal tract, Speech recognition, Autoregressive model, Computer science, Variance (accounting)

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