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首页> 外文期刊>Journal of computer sciences >LOW FOOTPRINT HIGH INTELLIGIBILITY MALAY SPEECH SYNTHESIZER BASED ON STATISTICAL DATA | Science Publications
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LOW FOOTPRINT HIGH INTELLIGIBILITY MALAY SPEECH SYNTHESIZER BASED ON STATISTICAL DATA | Science Publications

机译:基于统计数据的低足迹高智能马来语语音合成器|科学出版物

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> Speech synthesis plays a pivotal role nowadays. It can be found in various daily applications such as in mobile phones, navigation systems, languages learning software and so on. In this study, a Malay language speech synthesizer was designed using hidden Markov model to improve the performance of current Malay speech synthesizer and also extend Malay speech technology. Statistical parametric method was utilized in this study. The database was constructed to be balanced with all the phonetic sample appeared in Malay language. The results were rated by 48 listeners and obtained a moderate high rating ranging from 3.79 to 4.23 out of 5. The computed Word Error Rate is 7.1%. The total file size is less than 2 Megabytes which means it is suitable to be embedded into daily application. In conclusion, a Malay language speech synthesizer was designed using statistical parametric method with hidden Markov model. The output speech was verified to be good in quality. The file size is small indicates the feasibility to be used in embedded system.
机译: >语音合成在当今起着举足轻重的作用。可以在各种日常应用中找到它,例如在手机,导航系统,语言学习软件等中。在这项研究中,使用隐马尔可夫模型设计了一种马来语语音合成器,以改善当前的马来语语音合成器的性能,并扩展了马来语语音技术。本研究采用统计参数方法。该数据库旨在与所有以马来语显示的语音样本保持平衡。 48位听众对该结果进行了评分,并在5中的3.79到4.23之间获得了中度较高的评分。计算出的单词错误率是7.1%。文件总大小小于2 MB,这意味着它适合嵌入到日常应用程序中。总之,采用统计参数方法和隐马尔可夫模型设计了马来语语音合成器。输出的语音经验证质量良好。文件大小较小,表明在嵌入式系统中使用该文件的可行性。

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