A Recurrent Neural Network-Based Approach to Automatic Language Identification from Speech

被引：1

作者：

Mukherjee, Himadri ^{[1
]}

Dhar, Ankita ^{[1
]}

Obaidullah, Sk Md ^{[2
]}

Santosh, K. C. ^{[3
]}

Phadikar, Santanu ^{[4
]}

Roy, Kaushik ^{[1
]}

机构：

[1] West Bengal State Univ, Dept Comp Sci, Kolkata, India

[2] Aliah Univ, Dept Comp Sci & Engn, Kolkata, India

[3] Univ South Dakota, Dept Comp Sci, Brookings, SD USA

[4] Maulana Abul Kalam Azad Univ Technol, Dept Comp Sci & Engn, Kolkata, India

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMMUNICATION, DEVICES AND COMPUTING | 2020年 / 602卷

关键词：

Language identification; Recurrent neural network; Long short-term memory; Line spectral frequency;

D O I：

10.1007/978-981-15-0829-5_43

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The task of automatically identifying the used language from speech signals is known as automatic language identification. It is very much important prior to speech recognition in multilingual scenarios where speakers use more than a single language in course of communication. In this paper, a recurrent neural network (RNN)-based system with long short-term memory (LSTM) along with handcrafted line spectral frequency-based features is proposed for language identification. Experiments were performed on as many as 21908 clips (more than 30 h of data) from the top three spoken languages of the world, namely, English, Chinese, and Spanish, and a highest average accuracy of 95.22% has been obtained.

引用

页码：441 / 450

页数：10

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