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http://dx.doi.org/10.3745/KTSDE.2015.4.6.261

Performance Improvement Methods of a Spoken Chatting System Using SVM  

Ahn, HyeokJu (강원대학교 컴퓨터정보통신공학 전공)
Lee, SungHee (강원대학교 컴퓨터정보통신공학 전공)
Song, YeongKil (강원대학교 컴퓨터정보통신공학 전공)
Kim, HarkSoo (강원대학교 컴퓨터정보통신공학 전공)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.6, 2015 , pp. 261-268 More about this Journal
Abstract
In spoken chatting systems, users'spoken queries are converted to text queries using automatic speech recognition (ASR) engines. If the top-1 results of the ASR engines are incorrect, these errors are propagated to the spoken chatting systems. To improve the top-1 accuracies of ASR engines, we propose a post-processing model to rearrange the top-n outputs of ASR engines using a ranking support vector machine (RankSVM). On the other hand, a number of chatting sentences are needed to train chatting systems. If new chatting sentences are not frequently added to training data, responses of the chatting systems will be old-fashioned soon. To resolve this problem, we propose a data collection model to automatically select chatting sentences from TV and movie scenarios using a support vector machine (SVM). In the experiments, the post-processing model showed a higher precision of 4.4% and a higher recall rate of 6.4% compared to the baseline model (without post-processing). Then, the data collection model showed the high precision of 98.95% and the recall rate of 57.14%.
Keywords
Spoken Chatting System; Re-Rank Model; Data Collection Model; RankSVM; SVM;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 iOS 8-Siri [Internet], http://www.apple.com/kr/ios/siri/ (2014. 11. 26).
2 S-Voice [Internet], http://ko.wikipedia.org/wiki/S_보이스 (2014. 11. 26).
3 LG OptimusUI [Internet], http://ko.wikipedia.org/wiki/LG_옵티머스_UI (2014. 11. 26).
4 Ki-Seung Lee, "Study on the Improvement of Speech Recognizer by Using Time Scale Modification," The Journal of the Acoustical Society of Korea, Vol.23 No.6, pp.462-472, 2004.
5 Chang-young Lee, "Comparison of Male/Female Speech Features and Improvement of Recognition Performance by Gender-Specific Speech Recognition," Journal of The Korea Institute of Information and Communication Engineering, Vol.5, No.6, pp.568-574, 2010.
6 Jungho Cho, "A Spectral Compensation Method for Noise Robust Speech Recognition," Journal of the Institute of Electronics Engineers of Korea, Vol.49-IE, No.2, pp.9-17, 2012.
7 Sook-Nam Choi, Hyun-Yeol Chung, "Noise Robust Speech Recognition Based on Parallel Model Combination Adaptation Using Frequency-Variant," The Journal of the Acoustical Society of Korea, Vol.32, No.3, pp.252-261, 2013.   DOI
8 Tae-woong Choi, Soon-hyob Kim, "Gamma-tone Feature Extraction Acoustic Modeling for Improving Speech Recotnition Performance," The Korean Institute of Information Technology, Vol.10, No.11, pp.155-160, 2012.
9 Md. Afzal Hossan, Sheeraz Memon, and Mark A Gragory, "A Novel Approch for MFCC Feature Extraction," ICSPCS, pp.1-5, 2010.
10 DongHee Lim, SeungShik Kang, and DuSeong Chang, "Word Spacing Error Correction for the Postprocessing of Speech Recognition," Korea Computer Congress, Vol.33, No.1, pp.25-27, 2006.
11 WonMoon Song, EunJu Kim, and MyungWon Kim, "Post-Processing of Speech Recognition Using User Utterance Sequential Pattern," Korea Computer Congress, pp.709-711, 2005.
12 Thorsten Joachims, Support Vector Machine for Ranking, Cornell University, 2009, [Internet] http://www.cs.cornell.edu/people/tj/svm_light/svm_rank.html (2014.11.26).
13 Thorsten Joachims, Support Vector Machine(light), Cornell University, 2008, [Internet] http://svmlight.joachims.org/(2014. 11. 26).
14 Simsimi [Internet], http://developer.simsimi.com/2002 (2014. 11. 26).
15 Sejong Corpus [Internet], http://www.sejong.or.kr/ (2014. 11. 26).
16 Jonghwan Kim, Duseong Chang, and Harksoo Kim, "Statistical Generation of Korean Chatting Sentences Using Multiple Feature Information," Korean Journal of Cognitive Science, Vol.20, No.4, pp.421-437, 2009.   DOI