Data Augmentation for Automatic Speech Recognition for Low Resource Languages
Author | : Ronit Damania |
Publisher | : |
Total Pages | : 37 |
Release | : 2021 |
ISBN-10 | : OCLC:1306448860 |
ISBN-13 | : |
Rating | : 4/5 ( Downloads) |
Download or read book Data Augmentation for Automatic Speech Recognition for Low Resource Languages written by Ronit Damania and published by . This book was released on 2021 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this thesis, we explore several novel data augmentation methods for improving the performance of automatic speech recognition (ASR) on low-resource languages. Using a 100-hour subset of English LibriSpeech to simulate a low-resource setting, we compare the well-known SpecAugment augmentation approach to these new methods, along with several other competitive baselines. We then apply the most promising combinations of models and augmentation methods to three genuinely under-resourced languages using the 40-hour Gujarati, Tamil, Telugu datasets from the 2021 Interspeech Low Resource Automatic Speech Recognition Challenge for Indian Languages. Our data augmentation approaches, coupled with state-of-the-art acoustic model architectures and language models, yield reductions in word error rate over SpecAugment and other competitive baselines for the LibriSpeech-100 dataset, showing a particular advantage over prior models for the ``other'', more challenging, dev and test sets. Extending this work to the low-resource Indian languages, we see large improvements over the baseline models and results comparable to large multilingual models."--Abstract.