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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2011 >Phonological Knowledge Guided HMM State Mapping for Cross-Lingual Speaker Adaptation
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Phonological Knowledge Guided HMM State Mapping for Cross-Lingual Speaker Adaptation

机译：语音知识指导的HMM状态映射以实现跨语言说话者适应

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摘要

Within the HMM state mapping-based cross-lingual speaker adaptation framework, the minimum Kullback-Leibler divergence criterion has been typically employed to measure the similarity of two average voice state distributions from two respective languages for state mapping construction. Considering that this simple criterion doesn't take any language-specific information into account, we propose a data-driven, phonological knowledge guided approach to strengthen the mapping construction - state distributions from the two languages are clustered according to broad phonetic categories using decision trees and mapping rules are constructed only within each of the clusters. Objective evaluation of our proposed approach demonstrates reduction of mel-cepstral distortion and that mapping rules derived from a single training speaker generalize to other speakers, with subtle improvement being detected during subjective listening tests.
机译：在基于HMM状态映射的跨语言说话者适应框架内，最小Kullback-Leibler散度准则通常已用于测量来自两种相应语言的两种平均语音状态分布的相似性，以进行状态映射构建。考虑到此简单标准不考虑任何特定于语言的信息，我们提出了一种数据驱动的语音学知识指导方法，以加强映射构建-两种语言的状态分布根据决策树根据广泛的语音类别进行聚类和映射规则仅在每个群集中构造。对我们提出的方法进行的客观评估表明，该方法可以减少mel倒谱失真，并且可以将源自单个培训说话者的映射规则推广到其他说话者，并且在主观听力测试中可以发现微妙的改进。

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