Higher Accuracy of Hindi Speech Recognition Due to Online Speaker Adaptation

被引：0

作者：

Sivaraman, Ganesh ^{[1
]}

Malta, Swapnil ^{[2
]}

Nabar, Neeraj ^{[3
]}

Samudravijaya, K. ^{[4
]}

机构：

[1] Dept Elect & Elect, BITS Pilani KK Birla Goa Campus, Goa, India

[2] Natl Inst Technol, Dept Informat Technol, Durgapur, West Bengal, India

[3] PVPP Coll Engn, Dept Elect Engn, Mumbai, Maharashtra, India

[4] Tata Inst Fundamental Res, Sch Technol & Comp Sci, Mumbai, Maharashtra, India

来源：

TECHNOLOGY SYSTEMS AND MANAGEMENT | 2011年 / 145卷

关键词：

Automatic Speech Recognition (ASR); online speaker adaptation; Maximum Likelihood Linear Regression (MLLR); Hindi Speech recognition;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Speaker Adaptation is a technique which is used to improve the recognition accuracy of Automatic Speech Recognition (ASR) systems. Here, we report a study of the impact of online speaker adaptation on the performance of a speaker independent, continuous speech recognition system for Hindi language. The speaker adaptation is performed using the Maximum Likelihood Linear Regression (MLLR) transformation approach. The ASR system was trained using narrowband speech. The efficacy of the speaker adaptation is studied by using an unrelated speech database. The MLLR transform based speaker adaptation technique is found to significantly improve the accuracy of the Hindi ASR system by 3%.

引用

页码：233 / +

页数：2

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