The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments

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Automatic Speech Recognition & Understanding(2015)

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TL;DR

A first set of baseline results obtained using different techniques, including Deep Neural Networks (DNN), aligned with the state-of-the-art at international level are reported.

Abstract

This paper introduces the contents and the possible usage of the DIRHA-ENGLISH multi-microphone corpus, recently realized under the EC DIRHA project. The reference scenario is a domestic environment equipped with a large number of microphones and microphone arrays distributed in space. The corpus is composed of both real and simulated material, and it includes 12 US and 12 UK English native speakers. Each speaker uttered different sets of phonetically-rich sentences, newspaper articles, conversational speech, keywords, and commands. From this material, a large set of 1-minute sequences was generated, which also includes typical domestic background noise as well as inter/intra-room reverberation effects. Dev and test sets were derived, which represent a very precious material for different studies on multi-microphone speech processing and distant-speech recognition. Various tasks and corresponding Kaldi recipes have already been developed. The paper reports a first set of baseline results obtained using different techniques, including Deep Neural Networks (DNN), aligned with the state-of-the-art at international level.

Authors

M. Ravanelli

17 papers

L. Cristoforetti

1 papers

R. Gretter

1 papers

References35 items

Contaminated speech training methods for robust DNN-HMM distant speech recognition

The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

A multi-channel corpus for distant-speech interaction in presence of known interferences

Automatic Speech Recognition: A Deep Learning Approach

The DIRHA-ENGLISH corpus and related tasks for distant-speech recognition in domestic environments

Published in

Automatic Speech Recognition & Understanding(2015)

External Links:

Generate Graph DownloadPDF

TL;DR

A first set of baseline results obtained using different techniques, including Deep Neural Networks (DNN), aligned with the state-of-the-art at international level are reported.

Abstract

Authors

M. Ravanelli

17 papers

L. Cristoforetti

1 papers

R. Gretter

1 papers

References35 items

Contaminated speech training methods for robust DNN-HMM distant speech recognition

The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

A multi-channel corpus for distant-speech interaction in presence of known interferences

Automatic Speech Recognition: A Deep Learning Approach

Marco Pellin

1 papers

Alessandro Sosi

1 papers

TL;DR

Abstract

Authors

References35 items

Contaminated speech training methods for robust DNN-HMM distant speech recognition

The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

A multi-channel corpus for distant-speech interaction in presence of known interferences

Automatic Speech Recognition: A Deep Learning Approach

TL;DR

Abstract

Authors

References35 items

Contaminated speech training methods for robust DNN-HMM distant speech recognition

The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

Reverberant speech recognition combining deep neural networks and deep autoencoders augmented with a phone-class feature

A multi-channel corpus for distant-speech interaction in presence of known interferences

Automatic Speech Recognition: A Deep Learning Approach

Spatial diffuseness features for DNN-based speech recognition in noisy and reverberant environments

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