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Voice and speech recognition in Tamil language

机译:泰米尔语语音和语音识别

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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,我们的目的是在智能手机中创建语音和语音识别系统,以识别语音并捕获泰米尔语中的语音数据,并将捕获的语音存储并转换为泰米尔语本身的文本。它可以用于语音拨号,通过说出消息来发送SMS,并将捕获的消息发送给泰米尔语中的收件人。泰米尔语语言在智能手机的语音和语音识别中没有得到太多考虑。对于本地用户,泰米尔语语音识别和语音将为智能手机体验提供更大的灵活性。如果只使用泰米尔语,也只能使用母语泰米尔语的人会更容易在智能手机中使用语音识别系统。对于本地用户而言,使用电话将不再有困难,也无需学习使用智能电话。自动语音识别(ASR)系统在许多应用中都取得了巨大的成功。其中包括动态时间规整(DTW)等模板匹配技术,隐马尔可夫模型(HMM)和高斯混合模型(GMM)等统计模式匹配技术,神经网络(NN)等机器学习技术,支持向量机(SVM) )和决策树(DT)最为流行。对于该系统,使用HMM技术可获得最高的单词识别精度。它在培训过程中提供了100%的准确性,而在测试过程中提供了约98%的准确性。

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