# Robust Front-End for Multi-Channel ASR using Flow-Based Density Estimation

arXiv: https://arxiv.org/abs/2007.12903 · HTML: https://mlsanity.com/paper/2007.12903/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 9 of 9)

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- [NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2106.02210/) (2021)
- [VAE based Text Style Transfer with Pivot Words Enhancement Learning](https://mlsanity.com/paper/2112.03154/) (2021)
- [SE-DAE: Style-Enhanced Denoising Auto-Encoder for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2104.12977/) (2021)
- [Molecular Attributes Transfer from Non-Parallel Data](https://mlsanity.com/paper/2111.15146/) (2021)
- [On Text Style Transfer via Style Masked Language Models](https://mlsanity.com/paper/2210.06394/) (2022)

## Abstract (arXiv)

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.