Artificial intelligence, Speech recognition, Natural language processing, Mandarin Chinese and Vocabulary are his primary areas of study. He has researched Artificial intelligence in several fields, including Adaptation and Pattern recognition. His Speech recognition research is multidisciplinary, relying on both Sentence, Unsupervised learning, Autoencoder and Word.
His Natural language processing research includes elements of Search engine indexing and Speech synthesis. His work deals with themes such as Dictation, Chinese characters, Chinese language, Character and Syllable, which intersect with Mandarin Chinese. His research investigates the connection with Vocabulary and areas like Syllabic verse which intersect with concerns in Mandarin speech recognition.
His main research concerns Artificial intelligence, Speech recognition, Natural language processing, Mandarin Chinese and Pattern recognition. His Artificial intelligence study typically links adjacent topics like Chinese language. His Speech recognition research focuses on Vocabulary and how it relates to Speech corpus.
His work carried out in the field of Natural language processing brings together such families of science as Document retrieval and Information retrieval. His studies deal with areas such as Structure and Key as well as Information retrieval. His Mandarin Chinese research integrates issues from Dictation, Chinese characters, Speech synthesis, Character and Tone.
Lin-Shan Lee spends much of his time researching Speech recognition, Artificial intelligence, Natural language processing, Term and Hidden Markov model. His Speech recognition study combines topics in areas such as Artificial neural network, Autoencoder and Feature. Artificial intelligence is frequently linked to Pattern recognition in his study.
His Natural language processing study integrates concerns from other disciplines, such as Semantics, Recurrent neural network and Word. His Hidden Markov model research incorporates themes from Transcription and Mandarin Chinese. His research in Mandarin Chinese intersects with topics in Consistency and Space.
Lin-Shan Lee mostly deals with Artificial intelligence, Speech recognition, Natural language processing, Language model and Autoencoder. His biological study spans a wide range of topics, including Key and Pattern recognition. The various areas that he examines in his Speech recognition study include Encoder, Cluster analysis, Feature vector, Decoding methods and Unsupervised learning.
His biological study spans a wide range of topics, including Pronunciation, Recurrent neural network, First language, Query expansion and Markov decision process. In his study, Vocabulary, Lexicon, Computational linguistics and Mandarin Chinese is inextricably linked to Spoken language, which falls within the broad field of Language model. His research in Autoencoder intersects with topics in Segmentation, Representation, Utterance and Word2vec.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Robust entropy-based endpoint detection for speech recognition in noisy environments.
Jia-Lin Shen;Jeih-Weih Hung;Lin-Shan Lee.
conference of the international speech communication association (1998)
A new framework for recognition of Mandarin syllables with tones using sub-syllabic units
C.-H. Lin;L.-S. Lee;P.-Y. Ting.
international conference on acoustics, speech, and signal processing (1993)
A DISTRIBUTED ARCHITECTURE FOR COOPERATIVE SPOKEN DIALOGUE AGENTS WITH COHERENT DIALOGUE STATE AND HISTORY
Bor-shen Lin;Hsin-min Wang;Lin-Shan Lee.
A real-time Mandarin dictation machine for Chinese language with unlimited texts and very large vocabulary
L.S. Lee;C.Y. Tseng;H.Y. Gu;F.H. Liu.
international conference on acoustics, speech, and signal processing (1990)
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)
Spoken document understanding and organization
Lin-shan Lee;B. Chen.
IEEE Signal Processing Magazine (2005)
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)
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)
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)
The synthesis rules in a Chinese text-to-speech system
Lin-Shan Lee;Chiu-Yu Tseng;Ming Ouh-Young.
IEEE Transactions on Acoustics, Speech, and Signal Processing (1989)
Profile was last updated on December 6th, 2021.
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