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Analysis of the Error Pattern of HMM based Bangla ASR

机译:基于孟加拉ASR的误差模式分析

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Speech Recognition research has been ongoing for more than 80 years. Various attempts have been made to develop and improve speech recognition process around the world. Research on ASR by machine has attracted much attention over the last few decades. Bengali is largely spoken all over the world. There are lots of scopes yet to explore in the research regarding offline automatic Bangla speech recognition system. In our work, a moderate size speech corpus and a HMM based speech recognizer have been built to analyze the error pattern. Audio recordings have been collected from different persons in both quiet and noisy area. Live test has been carried out also to check the performance of the model individually. The percentage of the error and the percentage of correction with the created models are presented in this paper along with the results obtained during the live test. Finally, the results are analyzed to get the error pattern needed for future development.
机译:语音识别研究已经持续了80多年。已经进行了各种尝试,以发展和改进世界各地的语音识别过程。在过去的几十年里,机器的ASR研究引起了很多关注。孟加拉在很大程度上在世界各地都说。有很多范围尚未在关于离线自动Bangla语音识别系统的研究中探索。在我们的工作中,建立了一个中等大小的语音语料库和基于嗯的语音识别器来分析错误模式。在安静和嘈杂地区的不同人员中收集了录音。已经进行了实时测试,以单独检查模型的性能。本文介绍了误差的百分比和与所产生模型的校正百分比以及在实时测试期间获得的结果。最后,分析了结果以获得未来发展所需的错误模式。

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