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    • 51. 发明专利
    • SPEECH RECOGNITION APPARATUS
    • JP2000039899A
    • 2000-02-08
    • JP20748498
    • 1998-07-23
    • HITACHI LTD
    • KUJIRAI TOSHIHIROHATAOKA NOBUOAMANO AKIOODAKA TOSHIYUKIMURAMATSU RYUJIROMATSUDA TOSHIYUKISATO HITOSHI
    • G10L15/06G10L15/10
    • PROBLEM TO BE SOLVED: To enable speech recognition with a high recognition rate by classifying the inputted characteristic quantity vector strings which are previously subjected to buffering according to the vowel systems regarded to be likely by a collation section and utilizing these strings as the time series average of the characteristic quantity vector strings inputted with each of vowels. SOLUTION: A correction vector 904 is subtracted from the characteristic quantity vector string 901 and the corrected characteristic quantity vector string 902 is calculated in a correction vector subtraction section 101. The characteristic quantity vector string 902 is subjected to collation and likelihood calculation in the collation section 201 and a recognition candidate 903 is determined. On the other hand, the characteristic quantity vector string 901 for one utterance stored in a buffer 106 is classified by a clustering section 103 and the average for each vowel is calculated in an average calculation section 104. The difference between the standard pattern environmental characteristic quantity vectors and the average of each of the vowels is a value including the personal characteristics of the vowels and there is a correlation among the personal characteristics of each of the vowels. The speaker characteristics and the environmental characteristics are separated and estimated in a character sepn. estimation section 301 by utilizing this correlation.
    • 56. 发明专利
    • ADAPTIVE TYPE VOICE RECOGNIZING DEVICE
    • JPH1152978A
    • 1999-02-26
    • JP20577597
    • 1997-07-31
    • HITACHI LTD
    • OBUCHI YASUNARIAMANO AKIOODAKA TOSHIYUKIHATAOKA NOBUO
    • G10L15/06G10L15/10G10L3/00
    • PROBLEM TO BE SOLVED: To enable making an acoustic model having high accuracy by learning mutual relation between correction quantity of an acoustic models, and holding the result as previous knowledge. SOLUTION: A previous learning section 102 sends input of a teacher signal 108 and voice data 110 for previous learning to a after adaptation model making section 112. After that, the after adaptation model making section 112 compares a before adaptation acoustic model 114 with an after adaptation acoustic model 126, and outputs an acoustic model correction quantity 115. The acoustic model correction quantity 115 is sent to a previous knowledge extracting section 116, previous knowledge is extracted, and held in a previous knowledge holding section 118. Voice data 122 for adaptation collected in environment in which a recognizing device is actually used is inputted to an acoustic model adaptation section 104. Inputted voice data 129 for recognition is sent to a recognition executing section 130 with a word dictionary 128 for recognition in a voice recognizing section 106, these data are compared and collated with the after adaptation acoustic model 126, and a recognized result 132 is outputted.