Automatic Speech Recognition
Automatic Speech Recognition (ASR) aims to accurately transcribe spoken language into text, driving research into robust and efficient models. Current efforts focus on improving accuracy and robustness through techniques like consistency regularization in Connectionist Temporal Classification (CTC), leveraging pre-trained multilingual models for low-resource languages, and integrating Large Language Models (LLMs) for enhanced contextual understanding and improved handling of diverse accents and speech disorders. These advancements have significant implications for accessibility, enabling applications in diverse fields such as healthcare, education, and human-computer interaction.
Papers
AMPS: ASR with Multimodal Paraphrase Supervision
Amruta Parulekar, Abhishek Gupta, Sameep Chattopadhyay, Preethi Jyothi
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario
Shih-Heng Wang, Zih-Ching Chen, Jiatong Shi, Ming-To Chuang, Guan-Ting Lin, Kuan-Po Huang, David Harwath, Shang-Wen Li, Hung-yi Lee
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language
Muhammad Sharif, Zeeshan Abbas, Jiangyan Yi, Chenglin Liu
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM
Jiawei Yu, Yuang Li, Xiaosong Qiao, Huan Zhao, Xiaofeng Zhao, Wei Tang, Min Zhang, Hao Yang, Jinsong Su
Dialectal Coverage And Generalization in Arabic Speech Recognition
Amirbek Djanibekov, Hawau Olamide Toyin, Raghad Alshalan, Abdullah Alitr, Hanan Aldarmaki
Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages
Leena G Pillai, Kavya Manohar, Basil K Raju, Elizabeth Sherly