Speech Recognition Model for Assamese Language Using Deep Neural Network

Moirangthem Tiken Singh, Partha Pratim Barman, R. Gogoi
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Abstract

The work presents a speech recognition model for the Assamese language of the state of Assam of India. We experimented the model on the digits of Assamese language. The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks is the primary layer of our neural network model. We also use Mel Frequency Cepstral Coefficients for choosing the speech features. Finally, the accuracy of the model is evaluated based on the recognition rate.
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基于深度神经网络的阿萨姆语语音识别模型
这项工作提出了印度阿萨姆邦阿萨姆语的语音识别模型。我们在阿萨姆语的数字上实验了这个模型。利用深度神经网络建立识别模型。长短期记忆网络(LSTM)是神经网络模型的基础层,是由长短期记忆块组成的一种特殊的递归神经网络。我们还使用Mel频率倒谱系数来选择语音特征。最后,根据识别率对模型的准确率进行评价。
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