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Robust Front-End for Multi-Channel ASR using Flow-Based Density Estimation
Cited 1 time in
Web of Science
Cited 0 time in Scopus
- Authors
- Issue Date
- 2020-01
- Publisher
- IJCAI-INT JOINT CONF ARTIF INTELL
- Citation
- PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, pp.3744-3750
- Abstract
- For multi-channel speech recognition, speech enhancement techniques such as denoising or dereverberation are conventionally applied as a front-end processor. Deep learning-based front-ends using such techniques require aligned clean and noisy speech pairs which are generally obtained via data simulation. Recently, several joint optimization techniques have been proposed to train the front-end without parallel data within an end-to-end automatic speech recognition (ASR) scheme. However, the ASR objective is sub-optimal and insufficient for fully training the front-end, which still leaves room for improvement. In this paper, we propose a novel approach which incorporates flow-based density estimation for the robust front-end using non-parallel clean and noisy speech. Experimental results on the CHIME-4 dataset show that the proposed method outperforms the conventional techniques where the front-end is trained only with ASR objective.
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