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Chai Wutiwiwatchai

National Electronics and Computer Technology Center

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Nattanun Thatphithakkul

National Science and Technology Development Agency

Ananlada Chotimongkol

National Science and Technology Development Agency

Sawit Kasuriya

National Science and Technology Development Agency

Sadaoki Furui

Bridge University

Masashi Unoki

Japan Advanced Institute of Science and Technology

Sila Chunwijitra

National Electronics and Computer Technology Center

Vataya Chunwijitra

National Electronics and Computer Technology Center

Ausdang Thangthai

National Electronics and Computer Technology Center

Pakinee Aimmanee

Thammasat University

Sittipong Saychum

National Electronics and Computer Technology Center

Cooperative Institutions

National Electronics and Computer Technology Center

National Science and Technology Development Agency

Thammasat University

Chulalongkorn University

Tokyo Institute of Technology

King Mongkut's Institute of Technology Ladkrabang

National Institute of Information and Communications Technology

Japan Advanced Institute of Science and Technology

Kasetsart University

King Mongkut's University of Technology Thonburi

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Combination of finite state automata and neural network for spoken language understanding

Chai Wutiwiwatchai Sadaoki Furui

This paper proposes a novel approach for spoken language understanding based on a combination of weighted finite state automata and an artificial neural network.The former machine acts as a robust parser, which extracts some semantic information called subframes from an input sentence, then the latter machine interprets a concept of the sentence by considering the existence of subframes and their scores obtained from the automata.With a large number of concepts handled in our mixed-initiative dialogue system, the proposed system achieves a considerable concept interpretation result on either a typed-in test set or a spoken test set.A high subframe recall rate also verifies an applicability of the proposed system.

Spoken Language

10.21437/eurospeech.2003-736

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Citations (9)

Speech Analysis Method Based on Source-Filter Model Using Multivariate Empirical Mode Decomposition

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences (2016)

Surasak Boonkla Masashi Unoki Stanislav S. Makhanov Chai Wutiwiwatchai

We propose a speech analysis method based on the source-filter model using multivariate empirical mode decomposition (MEMD). The proposed method takes multiple adjacent frames of a speech signal into account by combining their log spectra into multivariate signals. The multivariate signals are then decomposed into intrinsic mode functions (IMFs). The IMFs are divided into two groups using the peak of the autocorrelation function (ACF) of an IMF. The first group characterized by a spectral fine structure is used to estimate the fundamental frequency F0 by using the ACF, whereas the second group characterized by the frequency response of the vocal-tract filter is used to estimate formant frequencies by using a peak picking technique. There are two advantages of using MEMD: (i) the variation in the number of IMFs is eliminated in contrast with single-frame based empirical mode decomposition and (ii) the common information of the adjacent frames aligns in the same order of IMFs because of the common mode alignment property of MEMD. These advantages make the analysis more accurate than with other methods. As opposed to the conventional linear prediction (LP) and cepstrum methods, which rely on the LP order and cut-off frequency, respectively, the proposed method automatically separates the glottal-source and vocal-tract filter. The results showed that the proposed method exhibits the highest accuracy of F0 estimation and correctly estimates the formant frequencies of the vocal-tract filter.

Vocal tract

Cepstrum

10.1587/transfun.e99.a.1762

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Citations (1)

ทรัพยากรขอความภาษาไทย: ชุดขอความภาษาไทยที่ควรใช สําหรับการวัดคุณภาพเสียงและการศึกษาเปรียบเทียบ Thai Text Resource: A Recommended Thai Text Set for Voice Quality Measurements and Its Comparative Study

Therdpong Daengsi Apiruck Preechayasomboon Saowanit Sukparungsee Chai Wutiwiwatchai

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Citations (2)

Thai speech processing technology: A review

Speech Communication (2006)

Chai Wutiwiwatchai Sadaoki Furui

Chinese speech synthesis

Spelling

Spoken Language

10.1016/j.specom.2006.10.004

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SSA-based audio-information-hiding scheme with psychoacoustic model

Jessada Karnjana Masashi Unoki Pakinee Aimmanee Chai Wutiwiwatchai

This paper proposes an inaudible and robust audio-information-hiding scheme based on the singular-spectrum analysis (SSA) and a psychoacoustic model. SSA is used to decompose the host signals into several additive oscillatory components. The hidden information is embedded into the host signals by modifying amplitudes of some oscillatory components. To satisfy the inaudibility, we propose a novel method which is based on the psychoacoustic model to choose the oscillatory components that distort the host signal only slightly after the modification. Accordingly, to associate the output from the psychoacoustic model with the SSA-based scheme, the relationship between the frequency components and singular-value indices is established. The test results show that the proposed scheme achieves both inaudibility and robustness. In addition, compared with the previously proposed scheme based on automatic parameterized SSA using the differential evolution, this proposed scheme has more advantage in terms of reducing the computational time.

Psychoacoustics

10.1109/apsipa.2016.7820686

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LOTUS-BN: A Thai broadcast news corpus and its research applications

Ananlada Chotimongkol K. Saykhum Patcharika Chootrakool Nattanun Thatphithakkul Chai Wutiwiwatchai

This paper describes the design and construction of the LOTUS-BN corpus, a Thai television broadcast news corpus. In addition to audio recordings and their transcription, this corpus also includes a detailed annotation of many interesting characteristics of broadcast news data such as acoustic condition, overlapping speech, news topic and named entity. The LOTUS-BN is still an ongoing project with the goal of collecting 100 hours of speech. We report initial statistics analyzed from 60 hours of speech which show that the LOTUS-BN corpus has a rich vocabulary of approximately 26,000 words with one third of them are named entities. Thus, this corpus is a good resource for developing an LVCSR system and investigating on named entity detection and recognition in addition to broadcast news related applications. Research applications on these topics are also discussed.

Corpus Linguistics

Transcription

Text corpus

10.1109/icsda.2009.5278377

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Citations (26)

Speech watermarking scheme based on singular-spectrum analysis for tampering detection and identification

2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (2017)

Jessada Karnjana Kasorn Galajit Pakinee Aimmanee Chai Wutiwiwatchai Masashi Unoki

This paper proposes a speech watermarking scheme for tampering detection and identification, which is based on the singular-spectrum analysis (SSA). Tampered signals can be detected and identified by analyzing signature information embedded into the speech signals. A required property of such hidden information is that it should be fragile to any malicious signal-processing operation. In this work, we use the SSA to analyze signals in embedding and extraction processes. The signature information is embedded into those signals by modifying a less-significant part of singular spectra. The evaluation results show that the proposed scheme is fragile to several malicious attacks but robust against non-malicious signal- processing operations. The sound quality of the watermarked signals also meets the inaudibility criteria. Besides detecting and locating the tampered locations, our proposed scheme can make a prediction about types and degrees of attacks. In addition, its new embedding rule enables one to fine-tune some parameters in order to improve the overall performance further.

Signature (topology)

Identification

SIGNAL (programming language)

Singular Spectrum Analysis

10.1109/apsipa.2017.8282027

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Citations (9)

Computers and the Thai Language

IEEE Annals of the History of Computing (2009)

Hugh Thaweesak Koanantakool Theppitak Karoonboonyanan Chai Wutiwiwatchai

This article explains the history of Thai language development for computers, examining such factors as the language, script, and writing system, among others. The article also analyzes characteristics of Thai characters and I/O methods, and addresses key issues involved in Thai text processing. Finally, the article reports on language processing research and provides detailed information on Thai language resources.Keywords: Thai language, syntax, Thai input method, Thai output method, Thai string collation, Thai cultural conventions, Thai natural language processing, Thai speech processing, search technology

Collation

Language identification

Text processing

10.1109/mahc.2009.5

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Citations (24)

Thai named-entity recognition using class-based language modeling on multiple-sized subword units

Interspeech 2022 (2008)

K. Saykhum Vataya Boonpiam Nattanun Thatphithakkul Chai Wutiwiwatchai Cholwich Natthee

Named Entity Recognition

Entity linking

Named entity

10.21437/interspeech.2008-263

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Citations (2)

Open Data Integration for Lanna Cultural Heritage e-Museums

Advances in intelligent systems and computing (2017)

Watchira Buranasing Marut Buranarach Chai Wutiwiwatchai

Data mapping

Linked Data

Metadata modeling

10.1007/978-3-319-70019-9_3

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Citations (2)

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