Attention based end to end Speech Recognition for Voice Search in Hindi and English

被引：4
|
作者：
Joshi, Raviraj [1 ]
Kannan, Venkateshan [1 ]
机构：
[1] Flipkart, Bengaluru, India
来源：
FIRE 2021: PROCEEDINGS OF THE 13TH ANNUAL MEETING OF THE FORUM FOR INFORMATION RETRIEVAL EVALUATION | 2021年
关键词：
automatic speech recognition; encoder-decoder models; attention; listen attend spell; SYSTEM; CHALLENGE;
D O I：
10.1145/3503162.3503173
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We describe here our work with automatic speech recognition (ASR) in the context of voice search functionality on the Flipkart e-Commerce platform. Starting with the deep learning architecture of Listen-Attend-Spell (LAS), we build upon and expand the model design and attention mechanisms to incorporate innovative approaches including multi-objective training, multi-pass training, and external rescoring using language models and phoneme based losses. We report a relative WER improvement of 15.7% on top of state-of-the-art LAS models using these modifications. Overall, we report an improvement of 36.9% over the phoneme-CTC system on the Flipkart Voice Search dataset. The paper also provides an overview of different components that can be tuned in a LAS based system.
引用
收藏
页码：107 / 113
页数：7
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