Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

Speech enhancement is a critical component of many user-oriented audio applications, yet
current systems still suffer from distorted and unnatural outputs. While generative models …

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …

Conventional automatic speech recognition (ASR) based on a hidden Markov model
(HMM)/deep neural network (DNN) is a very complicated system consisting of various …

Recently, there has been an increasing interest in end-to-end speech recognition that
directly transcribes speech to text without any predefined alignments. One approach is the …

Audio-based automatic speech recognition (ASR) degrades significantly in noisy
environments and is particularly vulnerable to interfering speech, as the model cannot …

The CHiME challenge series aims to advance robust automatic speech recognition (ASR)
technology by promoting research at the interface of speech and language processing …

This study proposes a complex spectral mapping approach for single-and multi-channel
speech enhancement, where deep neural networks (DNNs) are used to predict the real and …

This paper introduces a new open source platform for end-to-end speech processing named
ESPnet. ESPnet mainly focuses on end-to-end automatic speech recognition (ASR), and …