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
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation
Dena Mujtaba, Nihar R. Mahapatra, Megan Arney, J. Scott Yaruss, Caryn Herring, Jia Bin
Towards Effective and Efficient Non-autoregressive Decoding Using Block-based Attention Mask
Tianzi Wang, Xurong Xie, Zhaoqing Li, Shoukang Hu, Zengrui Jin, Jiajun Deng, Mingyu Cui, Shujie Hu, Mengzhe Geng, Guinan Li, Helen Meng, Xunying Liu
An efficient text augmentation approach for contextualized Mandarin speech recognition
Naijun Zheng, Xucheng Wan, Kai Liu, Ziqing Du, Zhou Huan
On the Encoding of Gender in Transformer-based ASR Representations
Aravind Krishnan, Badr M. Abdullah, Dietrich Klakow
Optimizing Byte-level Representation for End-to-end ASR
Roger Hsiao, Liuhui Deng, Erik McDermott, Ruchir Travadi, Xiaodan Zhuang
The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments
Shareef Babu Kalluri, Prachi Singh, Pratik Roy Chowdhuri, Apoorva Kulkarni, Shikha Baghel, Pradyoth Hegde, Swapnil Sontakke, Deepak K T, S. R. Mahadeva Prasanna, Deepu Vijayasenan, Sriram Ganapathy
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related Tasks
Amit Meghanani, Thomas Hain
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition
William Ravenscroft, George Close, Stefan Goetze, Thomas Hain, Mohammad Soleymanpour, Anurag Chowdhury, Mark C. Fuhs
EffectiveASR: A Single-Step Non-Autoregressive Mandarin Speech Recognition Architecture with High Accuracy and Inference Speed
Ziyang Zhuang, Chenfeng Miao, Kun Zou, Ming Fang, Tao Wei, Zijian Li, Ning Cheng, Wei Hu, Shaojun Wang, Jing Xiao
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets
Jiatong Shi, Shih-Heng Wang, William Chen, Martijn Bartelds, Vanya Bannihatti Kumar, Jinchuan Tian, Xuankai Chang, Dan Jurafsky, Karen Livescu, Hung-yi Lee, Shinji Watanabe
Towards Unsupervised Speech Recognition Without Pronunciation Models
Junrui Ni, Liming Wang, Yang Zhang, Kaizhi Qian, Heting Gao, Mark Hasegawa-Johnson, Chang D. Yoo
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion Techniques
Yuanchao Li, Peter Bell, Catherine Lai
Transformer-based Model for ASR N-Best Rescoring and Rewriting
Iwen E. Kang, Christophe Van Gysel, Man-Hung Siu
Guiding Frame-Level CTC Alignments Using Self-knowledge Distillation
Eungbeom Kim, Hantae Kim, Kyogu Lee
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding
Trang Le, Daniel Lazar, Suyoun Kim, Shan Jiang, Duc Le, Adithya Sagar, Aleksandr Livshits, Ahmed Aly, Akshat Shrivastava
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection
Rong Gong, Hongfei Xue, Lezhi Wang, Xin Xu, Qisheng Li, Lei Xie, Hui Bu, Shaomei Wu, Jiaming Zhou, Yong Qin, Binbin Zhang, Jun Du, Jia Bin, Ming Li
Tag and correct: high precision post-editing approach to correction of speech recognition errors
Tomasz Ziętkiewicz
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter
Andrei Andrusenko, Aleksandr Laptev, Vladimir Bataev, Vitaly Lavrukhin, Boris Ginsburg
Reading Miscue Detection in Primary School through Automatic Speech Recognition
Lingyun Gao, Cristian Tejedor-Garcia, Helmer Strik, Catia Cucchiarini