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Voice and speech recognition in Tamil language

机译:泰米尔语言中的语音和语音识别

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摘要

In our project, our intention is to create a voice and speech recognition system in smart phones that recognizes voice and captures the speech data in Tamil and stores and converts the captured speech as text in Tamil language itself. This can be used in voice dialing, sending SMS by saying out the message and the captured message is sent to the recipient in Tamil. There has not been much consideration for Tamil language to be used in voice and speech recognition in smart phones. For native users Tamil voice recognitions and speech would provide more flexibility in smart phone experience. Also people who have only been used to their native language Tamil, would feel easier to use the speech recognition system in their smart phones if provided in Tamil. There will be no more difficulty in usage of phones for local users and there is no need for any learning to use the smart phone. Automatic Speech Recognition (ASR) system have achieved a great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW), Statistical Pattern Matching techniques such as Hidden Markov Model (HMM) and Gaussian Mixture Models (GMM), Machine Learning techniques such as Neural Networks (NN), Support Vector Machine (SVM), and Decision Trees (DT) are most popular. For this system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and approximately 98% for testing process.
机译:在我们的项目中,我们的目的是在识别语音的智能手机中创建一个语音和语音识别系统,识别语音并捕获泰米尔和存储的语音数据,并将捕获的语音转换为泰米尔语言本身的文本。这可以在语音拨号中使用,通过说出消息,并将捕获的消息发送到泰米尔的收件人。泰米尔语言没有考虑在智能手机中的语音和语音识别中使用。对于本机用户,泰米尔语音识别和语音将在智能手机体验中提供更多的灵活性。此外,只有习惯于母语泰米尔的人,如果在泰米尔中提供的话,似乎更容易在智能手机中使用语音识别系统。对本地用户的手机的使用不会有更多困难,并且不需要任何学习使用智能手机。自动语音识别(ASR)系统在许多应用中取得了巨大的成功。其中,模板匹配技术,如动态时间翘曲(DTW),统计模式匹配技术,如隐藏的马尔可夫模型(HMM)和高斯混合模型(GMM),机器学习技术,如神经网络(NN),支持向量机(SVM) ),决策树(DT)最受欢迎。对于该系统,以HMM技术实现最高的字识别精度。它在培训过程中提供了100 %的准确性,并且测试过程约为98 %。

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