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    • 4. 发明授权
    • Method for transforming HMMs for speaker-independent recognition in a noisy environment
    • 用于在嘈杂环境中转换用于说话者无关识别的HMM的方法
    • US06658385B1
    • 2003-12-02
    • US09501879
    • 2000-02-10
    • Yifan GongJohn J. Godfrey
    • Yifan GongJohn J. Godfrey
    • G10L1506
    • G10L15/07G10L15/20
    • On improved transformation method uses an initial set of Hidden Markov Models (HMMs) trained on a large amount of speech recorded in a low noise environment R to provide rich information on co-articulation and speaker variation and a smaller database in a more noisy target environment T. A set H of HMMs is trained with data provided in the low noise environment R and the utterances in the noisy environment T are transcribed phonetically using set H of HMMs. The transcribed segments are grouped into a set of Classes C. For each subclass c of Classes C, the transformation &PHgr;c is found to maximize likelihood utterances in T, given H. The HMMs are transformed and steps repeated until likelihood stabilizes.
    • 改进的变换方法使用在低噪声环境R中记录的大量语音上训练的初始隐藏马尔科夫模型(HMM),以提供关于共同关节和说话者变化的丰富信息,并且在更嘈杂的目标环境中提供较小的数据库 用低噪声环境R中提供的数据训练HMM的集合H,并且在噪声环境中的话语T使用HMM的集合H在语音上被转录。 转录的段被分组为一组类C.对于C类的每个子类c,发现变换Phic在给定H的情况下最大化T中的似然性话语.HMM被转换并且重复步骤,直到可能性稳定。