Japanese broadcast news transcription and information extraction
Sadaoki Furui, Katsutoshi Ohtsuki, Zhipeng Zhang · Communications of the ACM · 2000
The article discusses developing a system for close-captioning and automatic information extraction for Japanese broadcast news speech Biblioteca de Ciencias y Tecnología Normal Biblioteca de Ciencias y Tecnología 1 0 2006-05-24T21:41:00Z 2006-05-24T21:41:00Z 1 144 793 UCLA 6 1 936 11.6568 Clean Clean 21 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} The article discusses developing a system for close-captioning and automatic information extraction for Japanese broadcast news speech. The article presents a large-vocabulary, continuous-speech recognition system for Japanese broadcast news speech transcription. It is part of a joint research project with NHK broadcasting whose goal is the closed-captioning of TV programs. While some of these problems investigated are Japanese-specific, others are language independent. A work-frequency list was derived for the news manuscripts, and the 20,000 most frequently used words were selected as vocabulary words. The feature vector consisted of 16 cepstral coefficients, normalized logarithmic power, and their delta features. Cepstral coefficients were normalized by the cepstral mean subtraction method. The acoustic models were gender-dependent shared-state triphone hidden Markov models and were designed using tree-based clustering.