Kenji Kita


Construction of MBTI Personality Estimation Model Considering Emotional Information
Ryota Kishima | Kazuyuki Matsumoto | Minoru Yoshida | Kenji Kita
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation


Visualization of the occurrence trend of infectious diseases using Twitter
Ryusei Matsumoto | Minoru Yoshida | Kazuyuki Matsumoto | Hironobu Matsuda | Kenji Kita
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


Emotion Estimation from Sentence Using Relation between Japanese Slangs and Emotion Expressions
Kazuyuki Matsumoto | Kenji Kita | Fuji Ren
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation


Exploring Emotional Words for Chinese Document Chief Emotion Analysis
Yunong Wu | Kenji Kita | Fuji Ren | Kazuyuki Matsumoto | Xin Kang
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation


Learning Nonstructural Distance Metric by Minimum Cluster Distortion
Daichi Mochihashi | Genichiro Kikui | Kenji Kita
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing


Machine-Readable Dictionary Headwords
Yasuhito Tanaka | Kenji Kita
Proceedings of the 12th Pacific Asia Conference on Language, Information and Computation


Probabilistic Language Modeling Based On Mixture Probabilistic Context-Free Grammar
Kenji Kita | Tatsuya Iwasa
Proceedings of Rocling VIII Computational Linguistics Conference VIII

Error Correction of Speech Recognition Outputs Using Generalized LR Parsing and Confusion Matrix
Tatsuya Iwasa | Kenji Kita
ROCLING 1995 Poster Papers


Processing Unknown Words in Continuous Speech Recognition
Kenji Kita | Terumasa Ehara | Tsuyoshi Morimoto
Proceedings of the Second International Workshop on Parsing Technologies

Current continuous speech recognition systems essentially ignore unknown words. Systems are designed to recognize words in the lexicon. However, for using speech recognition systems in real applications of spoken-language processing, it is very important to process unknown words. This paper proposes a continuous speech recognition method which accepts any utterance that might include unknown words. In this method, words not in the lexicon are transcribed as phone sequences, while words in the lexicon are recognized correctly. The HMM-LR speech recognition system, which is an integration of Hidden Markov Models and generalized LR parsing, is used as the baseline system, and enhanced with the trigram model of syllables to take into account the stochastic characteristics of a language. Preliminary results indicate that our approach is very promising.


Parsing Continuous Speech by HMM-LR Method
Kenji Kita | Takeshi Kawabata | Hiroaki Saito
Proceedings of the First International Workshop on Parsing Technologies

This paper describes a speech parsing method called HMM-LR. In HMM-LR, an LR parsing table is used to predict phones in speech input, and the system drives an HMM-based speech recognizer directly without any intervening structures such as a phone lattice. Very accurate, efficient speech parsing is achieved through the integrated processes of speech recognition and language analysis. The HMM-LR m ethod is applied to large-vocabulary speaker-dependent Japanese phrase recognition. The recognition rate is 87.1% for the top candidates and 97.7% for the five best candidates.