Hsin-Min Wang mainly focuses on Speech recognition, Artificial intelligence, Natural language processing, Mandarin Chinese and Syllable. His study in Speech recognition is interdisciplinary in nature, drawing from both Mixture model, Vocabulary and Generative model. His studies in Artificial intelligence integrate themes in fields like Decoding methods, Cable television and Pattern recognition.
His studies deal with areas such as Document retrieval, Search engine indexing and Hidden Markov model as well as Natural language processing. Hsin-Min Wang interconnects The Internet, Natural language and Speech processing in the investigation of issues within Mandarin Chinese. His Syllable study incorporates themes from Speech corpus and Chinese language.
His primary areas of investigation include Artificial intelligence, Speech recognition, Natural language processing, Pattern recognition and Mandarin Chinese. His work in Artificial intelligence tackles topics such as Machine learning which are related to areas like Training set. The concepts of his Speech recognition study are interwoven with issues in Speech enhancement and Vocabulary.
The Natural language processing study combines topics in areas such as Context and Information retrieval, Relevance, Search engine indexing. His study looks at the relationship between Pattern recognition and topics such as Cluster analysis, which overlap with Bayesian information criterion. His Syllable research incorporates elements of Natural language and Chinese language.
His primary scientific interests are in Speech recognition, Artificial intelligence, Speech enhancement, Deep learning and Autoencoder. His Speech recognition research is mostly focused on the topic Language model. Hsin-Min Wang interconnects Acoustic model and Mandarin Chinese in the investigation of issues within Language model.
In the field of Artificial intelligence, his study on Training set overlaps with subjects such as Naturalness. His Speech enhancement research is multidisciplinary, relying on both Intelligibility, Noise measurement, Noise reduction and Audio visual. In his research, Classifier and Similarity is intimately related to Code, which falls under the overarching field of Autoencoder.
His scientific interests lie mostly in Speech recognition, Artificial intelligence, Speech enhancement, Deep learning and Autoencoder. As a member of one scientific family, Hsin-Min Wang mostly works in the field of Speech recognition, focusing on Artificial neural network and, on occasion, Signal-to-noise ratio, Reinforcement learning, Language model, Word error rate and Mandarin Chinese. His work in the fields of Artificial intelligence, such as Generative model, overlaps with other areas such as Naturalness.
His work carried out in the field of Speech enhancement brings together such families of science as Encoder, Noise reduction and Model selection. His Autoencoder research is multidisciplinary, incorporating elements of Transfer of learning, Code and Classifier, Pattern recognition. In the field of Pattern recognition, his study on Training set overlaps with subjects such as Similarity, Test data, Structure and Frame.
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A DISTRIBUTED ARCHITECTURE FOR COOPERATIVE SPOKEN DIALOGUE AGENTS WITH COHERENT DIALOGUE STATE AND HISTORY
Bor-shen Lin;Hsin-min Wang;Lin-Shan Lee.
Fluent speech prosody: Framework and modeling
Chiu-yu Tseng;Shao-huang Pin;Yehlin Lee;Hsin-min Wang.
Speech Communication (2005)
Voice conversion from non-parallel corpora using variational auto-encoder
Chin-Cheng Hsu;Hsin-Te Hwang;Yi-Chiao Wu;Yu Tsao.
asia pacific signal and information processing association annual summit and conference (2016)
An initial study on large-vocabulary continuous Mandarin speech recognition with limited training data based on sub-syllabic models
Hsin-min Wang;Renyuan Lyu;Jia-lin Shen;Lin-shan Lee.
Int. Computer Symposium (Hsin-chu, R.O.C) (1994)
Golden Mandarin(II)-an intelligent Mandarin dictation machine for Chinese character input with adaptation/learning functions
Lin-Shan Lee;Keh-Jiann Chen;Chiu-Yu Tseng;Renyuan Lyu.
international conference on speech image processing and neural networks (1994)
Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
Chin-Cheng Hsu;Hsin-Te Hwang;Yi-Chiao Wu;Yu Tsao.
arXiv: Computation and Language (2017)
MATBN: A Mandarin Chinese Broadcast News Corpus
Hsin-Min Wang;Berlin Chen;Jen-Wei Kuo;Shih-Sian Cheng.
International Journal of Computational Linguistics & Chinese Language Processing, Volume 10, Number 2, June 2005: Special Issue on Annotated Speech Corpora (2005)
Fast speaker adaptation using eigenspace-based maximum likelihood linear regression.
Kuan-Ting Chen;Wen-Wei Liau;Hsin-Min Wang;Lin-Shan Lee.
conference of the international speech communication association (2000)
Automatic singer recognition of popular music recordings via estimation and modeling of solo vocal signals
Wei-Ho Tsai;Hsin-Min Wang.
IEEE Transactions on Audio, Speech, and Language Processing (2006)
Complete recognition of continuous Mandarin speech for Chinese language with very large vocabulary using limited training data
Hsin-Min Wang;Tai-Hsuan Ho;Rung-Chiung Yang;Jia-Lin Shen.
IEEE Transactions on Speech and Audio Processing (1997)
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