Shingo Kuroiwa


Evaluation Framework for Distant-talking Speech Recognition under Reverberant Environments: newest Part of the CENSREC Series -
Takanobu Nishiura | Masato Nakayama | Yuki Denda | Norihide Kitaoka | Kazumasa Yamamoto | Takeshi Yamada | Satoru Tsuge | Chiyomi Miyajima | Masakiyo Fujimoto | Tetsuya Takiguchi | Satoshi Tamura | Shingo Kuroiwa | Kazuya Takeda | Satoshi Nakamura
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Recently, speech recognition performance has been drastically improved by statistical methods and huge speech databases. Now performance improvement under such realistic environments as noisy conditions is being focused on. Since October 2001, we from the working group of the Information Processing Society in Japan have been working on evaluation methodologies and frameworks for Japanese noisy speech recognition. We have released frameworks including databases and evaluation tools called CENSREC-1 (Corpus and Environment for Noisy Speech RECognition 1; formerly AURORA-2J), CENSREC-2 (in-car connected digits recognition), CENSREC-3 (in-car isolated word recognition), and CENSREC-1-C (voice activity detection under noisy conditions). In this paper, we newly introduce a collection of databases and evaluation tools named CENSREC-4, which is an evaluation framework for distant-talking speech under hands-free conditions. Distant-talking speech recognition is crucial for a hands-free speech interface. Therefore, we measured room impulse responses to investigate reverberant speech recognition. The results of evaluation experiments proved that CENSREC-4 is an effective database suitable for evaluating the new dereverberation method because the traditional dereverberation process had difficulty sufficiently improving the recognition performance. The framework was released in March 2008, and many studies are being conducted with it in Japan.


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Speaker Identification Method Using Earth Mover’s Distance for CCC Speaker Recognition Evaluation 2006
Shingo Kuroiwa | Satoru Tsuge | Masahiko Kita | Fuji Ren
International Journal of Computational Linguistics & Chinese Language Processing, Volume 12, Number 3, September 2007: Special Issue on Invited Papers from ISCSLP 2006


Machine Transliteration
Mohamed Abdel Fattah | Fuji Ren | Shingo Kuroiwa
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation

A Chinese Automatic Text Summarization system for mobile devices
Lei Yu | Mengge Liu | Fuji Ren | Shingo Kuroiwa
Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation


Learning Cooccurrences by using a Parser
Kazunori Matsumoto | Hiroshi Sakaki | Shingo Kuroiwa
Proceedings of the First International Workshop on Parsing Technologies

This paper describes two methods for the acquisition and utilization of lexical cooccurrence relationships. Under these method, cooccurrence relationships are obtained from two kinds of inputs: example sentences and the corresponding correct syntactic structure. The first of the two methods treats a set of governors each element of which is bound to a element of sister nodes set in a syntactic structure under consideration, as a cooccurrence relationship. In the second method, a cooccurrence relationship name and affiliated attribute names are manually given in the description of augmented rewriting rules. Both methods discriminate correctness of cooccurrence by the use of the correct syntactic structure mentioned above. Experiment is made for both methods to find if thus obtained cooccurrence relationship is useful for the correct analysis.