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Towards an Interactive Voice Agent for Singapore Hokkien

Authors:

Vanessa Lim,

Hui Shan Ang,

Estelle Lee,

Boon Pang LimAuthors Info & Claims

HAI '16: Proceedings of the Fourth International Conference on Human Agent Interaction

Pages 249 - 252

https://doi.org/10.1145/2974804.2980495

Published: 04 October 2016 Publication History

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Abstract

Singapore Hokkien (SH) is the most commonly spoken non-Mandarin Chinese dialect in Singapore. It is an important language for many members of Singapore's pioneer generation, but much less so for the younger generation who prefer English. In recent years, the greying of this demographic has placed an increasing demand on for assistive devices to support them. We report ongoing efforts to build limited-vocabulary speech recognition, with the eventual goal of a conversational voice agent in SH that can support applications in home-automation or in-hospital use case scenarios. This process is challenging as sizeable SH speech corpora do not yet exist, and SH is sufficiently different from existing Mandarin or Minnan such that other corpora cannot be directly used. We document our efforts at building language resources -- audio corpora, pronunciation lexicons -- and present some preliminary findings on multilingual training.

References

[1]

David Deterding. 2007. Singapore English. Edinburgh: Edinburgh University Press.

Google Scholar

[2]

C.J. Chen, R. A. Gopinath, M. D. Monkowski, M. A. Picheny, K. Shen. 1997. New methods in continuous Mandarin speech recognition. In Proceedings of EUROSPEECH.

Google Scholar

[3]

Dau-Cheng Lyu, Min-Siong Liang, Yuangchin Chiang, Chun-Nan Hsu, Ren-Yuan Lyu. 2003. Large vocabulary Taiwanese (min-nan) speech recognition using tone features and statistical pronunciation modeling. In Proceedings of EUROSPEECH

Google Scholar

[4]

Amelia Hong. 2012. A Phonological and Phonetic Description of Singapore Hokkien. Master's Thesis. Nanyang Technological University, Singapore.

Google Scholar

[5]

Pinyin index - Chinese Dictionary of examples. In Discover China, learn Chinese Chinese-Tools.com. Retrieved on May 1st 2016 from http://www.chinesetools.com/chinese/examples/pinyin.html

Google Scholar

[6]

Daniel Povey et al. 2011. The Kaldi speech recognition toolkit. In Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding

Google Scholar

[7]

Geoffrey Hinton et al. Deep neural networks for acoustic modeling in speech recognition. Signal Processing Magazine, 2012

Google Scholar

[8]

Tanya Schulz, Alex Waibel. 1998 Multilingual and Crosslingual Speech Recognition, in Proc. DARPA Workshop on Broadcast News Transcription and Understanding

Google Scholar

[9]

Zhang, Y., Chuangsuwanich, E., & Glass, J. 2014. Language ID-based Training of Multilingual Stacked Bottleneck Features. In Proceedings of Interspeech, 1--5

Google Scholar

[10]

Ngoc Thang Vu, Florian Metze, Tanya Schulz. 2012. Multilingual Bottle-neck Features and its Application for Underresourced Languages, in Proceedings of Spoken Language Technology and Understanding

Google Scholar

[11]

Heigold, G., Vanhoucke, V., Senior, A., Nguyen, P., Ranzato, M., Devin, M., & Dean, J. 2013. Multilingual Acoustic Models Using Distributed Deep Neural Networks. In Proceedings of International Conference on Acoustics, Speech and Signal Processing, 8169--6149

Google Scholar

Cited By

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Do VChen NLim BHasegawa-Johnson M(2018)Multitask Learning for Phone Recognition of Underresourced Languages Using Mismatched TranscriptionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2017.278236026:3(501-514)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1109/TASLP.2017.2782360
Chen WHasegawa-Johnson MChen N(2018)Recognizing Zero-Resourced Languages Based on Mismatched Machine Transcriptions2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8462481(5979-5983)Online publication date: Apr-2018
https://doi.org/10.1109/ICASSP.2018.8462481

Index Terms

Towards an Interactive Voice Agent for Singapore Hokkien
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Information & Contributors

Information

Published In

HAI '16: Proceedings of the Fourth International Conference on Human Agent Interaction

October 2016

414 pages

ISBN:9781450345088

DOI:10.1145/2974804

General Chairs:
Wei Yun Yau
A*STAR Institute for Infocomm Research, Singapore
,
Takashi Omori
Tamagawa University, Japan
,
Program Chairs:
Giorgio Metta
Italian Institute of Technology, Italy
,
Hirotaka Osawa
University of Tsukuba, Japan
,
Shengdong Zhao
National University of Singapore, Singapore

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 October 2016

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HAI '16

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Chinese and Oriental Language Information Processing Society

HAI '16: The Fourth International Conference on Human Agent Interaction

October 4 - 7, 2016

Biopolis, Singapore

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HAI '16 Paper Acceptance Rate 29 of 182 submissions, 16%;

Overall Acceptance Rate 121 of 404 submissions, 30%

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Cited By

View all

Do VChen NLim BHasegawa-Johnson M(2018)Multitask Learning for Phone Recognition of Underresourced Languages Using Mismatched TranscriptionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2017.278236026:3(501-514)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1109/TASLP.2017.2782360
Chen WHasegawa-Johnson MChen N(2018)Recognizing Zero-Resourced Languages Based on Mismatched Machine Transcriptions2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2018.8462481(5979-5983)Online publication date: Apr-2018
https://doi.org/10.1109/ICASSP.2018.8462481

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Recommendations

Slovenian spontaneous speech recognition and acoustic modeling of filled pauses and onomatopoeas

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