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Method for transforming HMMs for speaker-independent recognition in a noisy environment
Method for transforming HMMs for speaker-independent recognition in a noisy environment
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机译:在嘈杂环境中转换用于说话人独立识别的HMM的方法
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摘要
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.
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机译:在改进的转换方法上,使用在低噪声环境R中记录的大量语音训练的初始隐马尔可夫模型(HMM)初始集合,以提供有关共发音和说话者变化的丰富信息,以及在噪声较大的目标环境中提供较小的数据库用在低噪声环境R中提供的数据训练HMM的集合H,并且使用HMM的集合H语音地记录在嘈杂环境T中的话语。转录的片段被分组为一组C类。对于C类的每个子类c,在给定H的情况下,发现变换&PHgr; c Sub>使T中的似然性最大化。HMM进行了变换和步骤重复,直到可能性稳定。
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