{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T05:21:56Z","timestamp":1737264116250,"version":"3.33.0"},"reference-count":16,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2007,3,22]],"date-time":"2007-03-22T00:00:00Z","timestamp":1174521600000},"content-version":"vor","delay-in-days":6289,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems &amp; Computers in Japan"],"published-print":{"date-parts":[[1990,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A human expert spectrogram reader is able to recognize phonemes in high\u2010accuracy performing phoneme segmentation and phoneme identification simultaneously using one's spectrogram reading knowledge. Spectrogram reading knowledge consists mainly of two parts: a strategic part for phoneme segmentation and identification and a pattern\u2010matching part. There are several knowledge\u2010based approaches in which all knowledge is implemented as rules. However, it is especially difficult to describe the pattern matching part of the knowledge as rules and to extract acoustic features automatically for phoneme identification. Here, we construct a phoneme recognition expert system which consists of two parts: (1) rule\u2010based phoneme segmentation, and (2) neural network\u2010based phoneme identification for knowledge such as pattern matching. This paper presents the architecture of the phoneme recognition expert system with its experimental result tested on Japanese consonants. The experimental result shows that 90.8 percent of the phonemes were segmented correctly and 92.4 percent of the phonemes were identified correctly within the correct segments, which means 83.9 percent of the phonemes were correctly recognized both in segmentation and identification.<\/jats:p>","DOI":"10.1002\/scj.4690211211","type":"journal-article","created":{"date-parts":[[2007,7,7]],"date-time":"2007-07-07T19:13:01Z","timestamp":1183835581000},"page":"101-111","source":"Crossref","is-referenced-by-count":2,"title":["Phoneme recognition expert system using spectrogram reading knowledge and neural networks"],"prefix":"10.1002","volume":"21","author":[{"given":"Yasuhiro","family":"Komori","sequence":"first","affiliation":[]},{"given":"Takeshi","family":"Kawabata","sequence":"additional","affiliation":[]},{"given":"Kaichiro","family":"Hatazaki","sequence":"additional","affiliation":[]},{"given":"Kiyohiro","family":"Shikano","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2007,3,22]]},"reference":[{"key":"e_1_2_1_2_2","doi-asserted-by":"crossref","unstructured":"V. W.ZueandR. A.Cole.Experiments on spectrogram reading. Proc. IEEE ICASSP pp.116\u2013119(1979).","DOI":"10.1109\/ICASSP.1979.1170735"},{"key":"e_1_2_1_3_2","doi-asserted-by":"crossref","unstructured":"N.Carbonell J. \u2010P.Damestoy D.Fohr J. \u2010P.Haton andF.Lonchamp.APHODEX: Design and implementation of an acoustic phonetic decoding expert system. Proc. IEEE ICASSP pp.1201\u20131204(1986).","DOI":"10.1109\/ICASSP.1986.1168799"},{"key":"e_1_2_1_4_2","doi-asserted-by":"crossref","unstructured":"V. W.ZueandL. F.Lamel.An expert spectrogram reader: A knowledge\u2010based approach to speech recognition. Proc. IEEE ICASSP pp.1197\u20131200(1986).","DOI":"10.1109\/ICASSP.1986.1168798"},{"key":"e_1_2_1_5_2","doi-asserted-by":"crossref","unstructured":"P. \u2010E.Stern M.Eskenazi andD.Memmi.An expert system for speech spectrogram reading. Proc. IEEE ICASSP pp.1193\u20131196(1986).","DOI":"10.1109\/ICASSP.1986.1168793"},{"key":"e_1_2_1_6_2","doi-asserted-by":"crossref","unstructured":"J. H.Connolly E. A.Edmonds J. J.Guzy S. R.Johnson andA.Woodcock.Automatic speech recognition based on spectrogram reading. Proc. IEEE ICASSP pp.611\u2013621(1986).","DOI":"10.1016\/S0020-7373(86)80012-8"},{"issue":"6","key":"e_1_2_1_7_2","first-page":"1189","article-title":"A Continuous Speech Recognition Expert System","volume":"70","author":"Mizoguchi R.","journal-title":"I. E. I. C. E., Japan"},{"key":"e_1_2_1_8_2","unstructured":"K.Hatazaki Y.Komori T.Kawabata andK.Shikano.Phoneme segmentation using spectrogram reading knowledge. Proc. IEEE ICASSP 89 pp.24. S8.2 (1989)."},{"key":"e_1_2_1_9_2","doi-asserted-by":"crossref","unstructured":"L. R.RabinerandB. H.Juang.An introduction to hidden Markov models. IEEE ASSP Mag. pp.4\u201316(Jan.1986).","DOI":"10.1109\/MASSP.1986.1165342"},{"key":"e_1_2_1_10_2","doi-asserted-by":"crossref","unstructured":"R. P.Lippmann.An introduction to computing with neural nets. IEEE ASSP Mag. pp.4\u201322(Apr.1987).","DOI":"10.1109\/MASSP.1987.1165576"},{"key":"e_1_2_1_11_2","doi-asserted-by":"crossref","unstructured":"K. F.LeeandH. W.Hon.Large vocabulary speaker\u2010independent continuous speech recognition using HMM. Proc. IEEE ICASSP88 pp.123\u2013126 S3.7 (1988).","DOI":"10.1016\/0167-6393(88)90053-2"},{"key":"e_1_2_1_12_2","unstructured":"A.Waibel. Phoneme Recognition Using Time\u2010Delay Neural Networks. Shingaku\u2010GihouSP87\u2013100(Dec.1987)."},{"volume-title":"ART Reference Manual","year":"1987","key":"e_1_2_1_13_2"},{"key":"e_1_2_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"volume-title":"Rule\u2010Based Expert Systems","year":"1985","author":"Buchanan B. G.","key":"e_1_2_1_15_2"},{"key":"e_1_2_1_16_2","unstructured":"H.Sawai A.Waibel M.Miyatake andK.Shikano.Phoneme Recognition by Scaling up Modular Time\u2010Delay Neural Networks. Shingaku\u2010GihouSP88\u2013105(Dec.1987)."},{"issue":"10","key":"e_1_2_1_17_2","article-title":"A Japanese speech database for various kinds of research purposes","volume":"44","author":"Takeda K.","year":"1988","journal-title":"Jour. 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