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Effectiveness of dereverberation techniques and system combination approach for various reverberant environments: REVERB challenge

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  • 残響除去手法とシステム統合手法の種々の残響環境に対する有効性: REVERBチャレンジ

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The recently released REVERB challenge includes a reverberant speech recognition task. This paper focuses on state-of-the-art ASR techniques such as discriminative training of acoustic models including Gaussian mixture model, sub-space Gaussian mixture model, and deep neural networks, and various feature transformations after the proposed single channel dereverberation method with reverberation time estimation and multi-channel beamforming that enhances direct sound compared with the reflected sound. In addition, because it is necessary to handle these various environments in the challenge and the best performing system is different from environment to environment, we perform a system combination approach using different feature and different types of systems. Moreover, we use our discriminative training technique for system combination that improves system combination by making systems complementary. Experiments show the effectiveness of these approaches, reaching 6.76% and 18.60% word error rate on the REVERB simulated and real test sets, which are 68.8% and 61.5% relative improvements over the baseline.


  • IPSJ SIG Notes

    IPSJ SIG Notes 2015 (6), 1-6, 2015-02-20

    Information Processing Society of Japan (IPSJ)


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