InforLorV4, Main, Exploration, bibRecord, 000963

A categorization of robust speech processing datasets

Identifieur interne : 000963 ( Main/Exploration ); précédent : 000962; suivant : 000964

A categorization of robust speech processing datasets

Auteurs : Jonathan Le Roux [États-Unis] ; Emmanuel Vincent [France]

Source :

RBID : Hal:hal-01063805

Abstract

Speech and audio signal processing research is a tale of data collection efforts and evaluation campaigns. While large datasets for automatic speech recognition (ASR) in clean environments with various speaking styles are available, the landscape is not as picture- perfect when it comes to robust ASR in realistic environments, much less so for evaluation of source separation and speech enhancement methods. Many data collection efforts have been conducted, moving along towards more and more realistic conditions, each mak- ing different compromises between mostly antagonistic factors: financial and human cost; amount of collected data; availability and quality of annotations and ground truth; natural- ness of mixing conditions; naturalness of speech content and speaking style; naturalness of the background noise; etc. In order to better understand what directions need to be explored to build datasets that best support the development and evaluation of algorithms for recognition, separation or localization that can be used in real-world applications, we present here a study of existing datasets in terms of their key attributes.

Url:

https://hal.inria.fr/hal-01063805

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000587
to stream Hal, to step Curation: 000587
to stream Hal, to step Checkpoint: 000899
to stream Main, to step Merge: 000965
to stream Main, to step Curation: 000963

Le document en format XML

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<front><div type="abstract" xml:lang="en">Speech and audio signal processing research is a tale of data collection efforts and evaluation campaigns. While large datasets for automatic speech recognition (ASR) in clean environments with various speaking styles are available, the landscape is not as picture- perfect when it comes to robust ASR in realistic environments, much less so for evaluation of source separation and speech enhancement methods. Many data collection efforts have been conducted, moving along towards more and more realistic conditions, each mak- ing different compromises between mostly antagonistic factors: financial and human cost; amount of collected data; availability and quality of annotations and ground truth; natural- ness of mixing conditions; naturalness of speech content and speaking style; naturalness of the background noise; etc. In order to better understand what directions need to be explored to build datasets that best support the development and evaluation of algorithms for recognition, separation or localization that can be used in real-world applications, we present here a study of existing datasets in terms of their key attributes.</div>
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A categorization of robust speech processing datasets

A categorization of robust speech processing datasets

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri