TOPIC SPECIFIC MODELS FOR TEXT FORMATTING AND SPEECH RECOGNITION
    1.
    发明公开
    TOPIC SPECIFIC MODELS FOR TEXT FORMATTING AND SPEECH RECOGNITION 有权
    议题的具体型号为文本格式和语音识别

    公开(公告)号:EP1687807A2

    公开(公告)日:2006-08-09

    申请号:EP04799133.6

    申请日:2004-11-12

    摘要: The present invention relates to a method, a computer system and a computer program product for speech recognition and/or text formatting by making use of topic specific statistical models. A text document which may be obtained from a first speech recognition pass is subject to segmentation and to an assignment of topic specific models for each obtained section. Each model of the set of models provides statistic information about language model probabilities, about text processing or formatting rules, as e.g. the interpretation of commands for punctuation, formatting, text highlighting or of ambiguous text portions requiring specific formatting, as well as a specific vocabulary being characteristic for each section of the recognized text. Furthermore, other properties of a speech recognition and/or formatting system (such as e.g. settings for the speaking rate) may be encoded in the statistical models. The models themselves are generated on the basis of annotated training data and/or by manual coding. Based on the assignment of models to sections of text an improved speech recognition and/or text formatting procedure is performed.

    LANGUAGE MODEL BASED ON THE SPEECH RECOGNITION HISTORY
    2.
    发明授权
    LANGUAGE MODEL BASED ON THE SPEECH RECOGNITION HISTORY 有权
    语言模型基于语音认知履历

    公开(公告)号:EP1055227B1

    公开(公告)日:2004-09-01

    申请号:EP99965513.7

    申请日:1999-12-16

    IPC分类号: G10L15/18

    CPC分类号: G10L15/197 G10L15/1815

    摘要: A small vocabulary pattern recognition system is used for recognizing a sequence of words, such as a sequence of digits (e.g. telephone number) or a sequence of commands. A representation of reference words is stored in a vocabulary 132, 134. Input means 110 are used for receiving a time-sequential input pattern representative of a spoken or written word sequence. A pattern recognizer 120 comprises a word-level matching unit 130 for generating a plurality of possible sequences of words by statistically comparing the input pattern to the representations of the reference words of the vocabulary 132, 134. A cache 150 is used for storing a plurality of most recently recognized words. A sequence-level matching unit 140 selects a word sequence from the plurality of sequences of words in dependence on a statistical language model which provides a probability of a sequence of M words, M>=2. The probability depends on a frequency of occurrence of the sequence in the cache. In this way for many small vocabulary systems where no reliable data is available on frequency of use of word sequences, the cache is used to provide data representative of the actual use.

    LANGUAGE MODEL BASED ON THE SPEECH RECOGNITION HISTORY
    4.
    发明公开
    LANGUAGE MODEL BASED ON THE SPEECH RECOGNITION HISTORY 有权
    语言模型基于语音认知履历

    公开(公告)号:EP1055227A1

    公开(公告)日:2000-11-29

    申请号:EP99965513.7

    申请日:1999-12-16

    IPC分类号: G10L15/18

    CPC分类号: G10L15/197 G10L15/1815

    摘要: A small vocabulary pattern recognition system is used for recognizing a sequence of words, such as a sequence of digits (e.g. telephone number) or a sequence of commands. A representation of reference words is stored in a vocabulary 132, 134. Input means 110 are used for receiving a time-sequential input pattern representative of a spoken or written word sequence. A pattern recognizer 120 comprises a word-level matching unit 130 for generating a plurality of possible sequences of words by statistically comparing the input pattern to the representations of the reference words of the vocabulary 132, 134. A cache 150 is used for storing a plurality of most recently recognized words. A sequence-level matching unit 140 selects a word sequence from the plurality of sequences of words in dependence on a statistical language model which provides a probability of a sequence of M words, M>=2. The probability depends on a frequency of occurrence of the sequence in the cache. In this way for many small vocabulary systems where no reliable data is available on frequency of use of word sequences, the cache is used to provide data representative of the actual use.