As one of Chinese minority languages,Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently.This,along with the relatively small Tibetan corpus,has resu...As one of Chinese minority languages,Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently.This,along with the relatively small Tibetan corpus,has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model.This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data.We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects:modeling unit selection,transfer learning method,and source language selection.Experimental results show that the Chinese-Tibetan multi-language learning method using multilanguage character set as the modeling unit yields the best performance on Tibetan Character Error Rate(CER)at 27.3%,which is reduced by 26.1%compared to the language-specific model.And our method also achieves the 2.2%higher accuracy using less amount of data compared with the method using Tibetan multi-dialect transfer learning under the same model structure and data set.展开更多
MFCC(Mel Frequency Cepstrum Coefficient)是语音处理过程中需要提取的重要频率参数之一,因其很好地模仿人耳的听觉感知,在各种语音识别和合成过程中得到广泛的应用。文章在分析标准的MFCC参数提取算法基础上,通过合并参数算法提取了...MFCC(Mel Frequency Cepstrum Coefficient)是语音处理过程中需要提取的重要频率参数之一,因其很好地模仿人耳的听觉感知,在各种语音识别和合成过程中得到广泛的应用。文章在分析标准的MFCC参数提取算法基础上,通过合并参数算法提取了藏语语音的MFCC,并给出了在Matlab里进行仿真实验的详细步骤。展开更多
基金This work was supported by three projects.Zhao Y received the Grant with Nos.61976236 and 2020MDJC06Bi X J received the Grant with No.20&ZD279.
文摘As one of Chinese minority languages,Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently.This,along with the relatively small Tibetan corpus,has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model.This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data.We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects:modeling unit selection,transfer learning method,and source language selection.Experimental results show that the Chinese-Tibetan multi-language learning method using multilanguage character set as the modeling unit yields the best performance on Tibetan Character Error Rate(CER)at 27.3%,which is reduced by 26.1%compared to the language-specific model.And our method also achieves the 2.2%higher accuracy using less amount of data compared with the method using Tibetan multi-dialect transfer learning under the same model structure and data set.
文摘MFCC(Mel Frequency Cepstrum Coefficient)是语音处理过程中需要提取的重要频率参数之一,因其很好地模仿人耳的听觉感知,在各种语音识别和合成过程中得到广泛的应用。文章在分析标准的MFCC参数提取算法基础上,通过合并参数算法提取了藏语语音的MFCC,并给出了在Matlab里进行仿真实验的详细步骤。
