Automatic Recognition of Kazakh Speech Using Deep Neural Networks

被引：14

作者：

Mamyrbayev, Orken ^{[1
]}

Turdalyuly, Mussa ^{[1
]}

Mekebayev, Nurbapa ^{[2
]}

Alimhan, Keylan ^{[1
]}

Kydyrbekova, Aizat ^{[2
]}

Turdalykyzy, Tolganay ^{[1
]}

机构：

[1] Inst Informat & Computat Technol, Alma Ata 050010, Kazakhstan

[2] al Farabi Kazakh Natl Univ, Alma Ata 050040, Kazakhstan

来源：

INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2019, PT II | 2019年 / 11432卷

关键词：

DNN; ASR; Kazakh speech recognition; LM;

D O I：

10.1007/978-3-030-14802-7_40

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents a deep neural network (DNN) system based on automatic speech recognition for Kazakh language, developed using the Kaldi speech recognition tool. DNNs are initialized using the restricted Boltzmann machines (RBM) and are trained using cross-entropy as the objective function and the standard back propagation of error. In order to achieve optimal results, the training has been modified based on peculiarities of Kazakh language. A 76 hours-corpus has been used in training. Results are compared for two different sets of values between classical models and various DNN settings.

引用

页码：465 / 474

页数：10

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