Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/248162
Title: | CODE-SWITCHING DETECTION TECHNIQUES AND LANGUAGE MODELING STRATEGIES FOR AUTOMATIC SPEECH RECOGNITION | Authors: | WANG QINYI | ORCID iD: | ![]() |
Keywords: | speech recognition, language model, code-switching, language identification, transformer, semi-supervised learning | Issue Date: | 5-Aug-2023 | Citation: | WANG QINYI (2023-08-05). CODE-SWITCHING DETECTION TECHNIQUES AND LANGUAGE MODELING STRATEGIES FOR AUTOMATIC SPEECH RECOGNITION. ScholarBank@NUS Repository. | Abstract: | Automatic speech recognition (ASR) systems have become an essential part of our daily lives. The advent of deep learning has brought about end-to-end ASR models, revolutionizing the field and achieving impressive performance. However, developing robust end-to-end ASR systems faces a significant hurdle due to the scarcity of paired speech-text data. Additionally, the practice of code-switching (CS) adds further complexity to speech recognition. This thesis aims to address these challenges by proposing innovative code-switching detection and language modeling strategies to enhance the performance of code-switching and end-to-end ASR systems. We first introduce a novel code-switching detection method to mitigate the over-switching issue in traditional CS ASR systems. Then we introduce speech-and-text Transformer to leverage unpaired text data and effectively address the challenges of catastrophic forgetting and model capacity gap prevalent in pre-training methods. Lastly, we address the challenge of language confusion in end-to-end CS ASR systems by leveraging text-derived language identities. | URI: | https://scholarbank.nus.edu.sg/handle/10635/248162 |
Appears in Collections: | Ph.D Theses (Open) |
Show full item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
Qinyi_Thesis.pdf | 4.05 MB | Adobe PDF | OPEN | None | View/Download |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.