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The Continuous Speech Recognition System Base on Hidden Markov Models with One-Stage Dynamic Programming Algorithm.

Based on Hidden Markov Models (HMM) with One-Stage Dynamic Programming Algorithm, a continuous-speech and speaker-independent Mandarin digit speech recognition system was designed in this work.
In order to implement this architecture to fit the performance of hardware, various parameters of speech characteristics were defined to optimize the process. Finally, the ¡§State Duration¡¨ and the ¡§Tone Transition Property Parameter¡¨ were extracted from speech temporal information to improve the recognition rate.
Via using the test database, experimental results show that this new ideal of one-stage dynamic programming algorithm , with ¡§state duration¡¨ and ¡§ tone transition property parameter¡¨ , will have 18% recognition rate increase when compare to the conventional one. For speaker-independent and connect-word recognition, this system will achieve recognition rate to 74%. For speaker-independent but isolate-word recognition, it will have recognition rate higher than 96%. Recognition rate of 92% is obtained as this system is applied to the connect-word speaker-dependent recognition.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0703103-001255
Date03 July 2003
CreatorsHsieh, Fang-Yi
ContributorsWu Yung-Chun, Huang Chin-Chin, Chern Tzuen-Lih, Wu Yin-Chin, Liu Chang-Huan
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageCholon
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0703103-001255
Rightsnot_available, Copyright information available at source archive

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