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- 2018
应用于短时语音语种识别的时长扩展方法
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Abstract:
为解决待识别语音时长小于10 s时，语种识别性能急剧下降的问题，该文提出应用语音时域伸缩（time-scale modification，TSM）技术改变语音的长度（从而改变了语速），并保持其他频域信息不变。首先，对一段待识别语音，应用TSM技术转换为多条时域压缩和时域拉伸后的语音；其次，将这些不同语速的语音与原语音拼接起来，生成一个时长较长的语音；最后，送入语种识别系统进行识别。实验结果表明：所提出的语音时长扩展算法可以显著提升短时语音的语种识别性能。
Abstract：The language recognition (LR) accuracy is often significantly reduced when the test utterance duration is as short as 10 s or less. This paper describes a method to extend the utterance length using time-scale modification (TSM) which changes the speech rate without changing the spectral information. The algorithm first converts an utterance to several time-stretched or time-compressed versions using TSM. These modified versions with different speech rates are concatenated together with the original one to form a long-duration signal, which is subsequently fed into the LR system. Tests demonstrate that this duration modification method dramatically improves the performance for short utterances.
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