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Constrained Subword Units for Speaker Recognition

机译:用于说话人识别的约束子词单位

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Phonetic features have been proposed to overcome performance degradation in spectral speaker recognition in difficult acoustic conditions. The harmful effect of those conditions, however, is not restricted to spectral systems but also affects the performance of the open-loop phone recognisers on which phonetic systems are based. In automatic speech recognition, larger subword units and the use of additional constraints from language models have been employed to improve robustness against adverse acoustic conditions. This paper evaluates the performance of more constrained phone recognition and different subword units for speaker recognition on heterogeneous broadcast data from German parliamentary speeches.rnUsing phone clusters and a strong language model instead of phones obtained from unconstrained recognition improves the equal error rate from 14.3% to 8.6% on the given data.
机译:已经提出了语音特征来克服在困难的声学条件下频谱说话者识别中的性能下降。但是,这些条件的有害影响不仅限于频谱系统,而且还会影响基于语音系统的开环电话识别器的性能。在自动语音识别中,已经采用了较大的子词单元并使用了语言模型中的其他约束来提高针对不利声学条件的鲁棒性。本文针对来自德国议会演讲的异构广播数据,评估了更受限的电话识别和不同的子词单元用于说话人识别的性能。使用电话群和强大的语言模型代替不受约束的识别所获得的电话,可以将等错误率从14.3%提高到给定数据的8.6%。

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