@inproceedings{song-etal-2019-chameleon,
title = "{C}hameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech",
author = "Song, Yuanfeng and
Jiang, Di and
Zhao, Weiwei and
Xu, Qian and
Wong, Raymond Chi-Wing and
Yang, Qiang",
editor = "Pad{\'o}, Sebastian and
Huang, Ruihong",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-3007",
doi = "10.18653/v1/D19-3007",
pages = "37--42",
abstract = "Language model is a vital component in modern automatic speech recognition (ASR) systems. Since {``}one-size-fits-all{''} language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem. In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques. This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them. With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion. We wish this demonstration would inspire more research on better language model techniques for ASR.",
}
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<abstract>Language model is a vital component in modern automatic speech recognition (ASR) systems. Since “one-size-fits-all” language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem. In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques. This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them. With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion. We wish this demonstration would inspire more research on better language model techniques for ASR.</abstract>
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%0 Conference Proceedings
%T Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech
%A Song, Yuanfeng
%A Jiang, Di
%A Zhao, Weiwei
%A Xu, Qian
%A Wong, Raymond Chi-Wing
%A Yang, Qiang
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F song-etal-2019-chameleon
%X Language model is a vital component in modern automatic speech recognition (ASR) systems. Since “one-size-fits-all” language model works suboptimally for conversational speeches, language model adaptation (LMA) is considered as a promising solution for solving this problem. In order to compare the state-of-the-art LMA techniques and systematically demonstrate their effect in conversational speech recognition, we develop a novel toolkit named Chameleon, which includes the state-of-the-art cache-based and topic-based LMA techniques. This demonstration does not only vividly visualize underlying working mechanisms of a variety of the state-of-the-art LMA models but also provide an interface for the user to customize the hyperparameters of them. With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion. We wish this demonstration would inspire more research on better language model techniques for ASR.
%R 10.18653/v1/D19-3007
%U https://aclanthology.org/D19-3007
%U https://doi.org/10.18653/v1/D19-3007
%P 37-42
Markdown (Informal)
[Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech](https://aclanthology.org/D19-3007) (Song et al., EMNLP-IJCNLP 2019)
ACL