WTASR: Wavelet Transformer for Automatic Speech Recognition of Indian Languages

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2022-11-24 DOI:10.26599/BDMA.2022.9020017

Tripti Choudhary;Vishal Goyal;Atul Bansal

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引用次数: 4

Abstract

Automatic speech recognition systems are developed for translating the speech signals into the corresponding text representation. This translation is used in a variety of applications like voice enabled commands, assistive devices and bots, etc. There is a significant lack of efficient technology for Indian languages. In this paper, an wavelet transformer for automatic speech recognition (WTASR) of Indian language is proposed. The speech signals suffer from the problem of high and low frequency over different times due to variation in speech of the speaker. Thus, wavelets enable the network to analyze the signal in multiscale. The wavelet decomposition of the signal is fed in the network for generating the text. The transformer network comprises an encoder decoder system for speech translation. The model is trained on Indian language dataset for translation of speech into corresponding text. The proposed method is compared with other state of the art methods. The results show that the proposed WTASR has a low word error rate and can be used for effective speech recognition for Indian language.

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WTASR：用于印度语语音自动识别的小波变换器

开发了用于将语音信号翻译成相应文本表示的自动语音识别系统。这种翻译用于各种应用程序，如语音命令、辅助设备和机器人等。印度语言严重缺乏有效的技术。本文提出了一种用于印度语自动语音识别的小波变换器。由于讲话者的语音变化，语音信号在不同时间上存在高频和低频的问题。因此，小波使网络能够以多尺度分析信号。信号的小波分解被馈送到网络中用于生成文本。变换器网络包括用于语音翻译的编码器-解码器系统。该模型在印度语言数据集上进行训练，用于将语音翻译成相应的文本。将所提出的方法与其他现有技术的方法进行比较。结果表明，所提出的WTASR具有较低的误字率，可用于印度语的有效语音识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.