Speech Translation
Speech translation (ST) aims to automatically convert spoken language in one language into written or spoken text in another, bridging communication barriers. Current research heavily utilizes large language models (LLMs) integrated with speech foundation models (SFMs), often employing techniques like chain-of-thought prompting and multimodal approaches to improve accuracy and reduce latency, particularly in simultaneous ST. These advancements are significant for improving cross-lingual communication in various applications, from real-time interpretation to accessibility tools, and are driving innovation in both model architectures and evaluation methodologies.
Papers
DiariST: Streaming Speech Translation with Speaker Diarization
Mu Yang, Naoyuki Kanda, Xiaofei Wang, Junkun Chen, Peidong Wang, Jian Xue, Jinyu Li, Takuya Yoshioka
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders
Heng-Jui Chang, Ning Dong, Ruslan Mavlyutov, Sravya Popuri, Yu-An Chung
Direct Text to Speech Translation System using Acoustic Units
Victoria Mingote, Pablo Gimeno, Luis Vicente, Sameer Khurana, Antoine Laurent, Jarod Duret
Learning When to Speak: Latency and Quality Trade-offs for Simultaneous Speech-to-Speech Translation with Offline Models
Liam Dugan, Anshul Wadhawan, Kyle Spence, Chris Callison-Burch, Morgan McGuire, Victor Zordan
Improved Cross-Lingual Transfer Learning For Automatic Speech Translation
Sameer Khurana, Nauman Dawalatabad, Antoine Laurent, Luis Vicente, Pablo Gimeno, Victoria Mingote, James Glass
Translatotron 3: Speech to Speech Translation with Monolingual Data
Eliya Nachmani, Alon Levkovitch, Yifan Ding, Chulayuth Asawaroengchai, Heiga Zen, Michelle Tadmor Ramanovich
CTC-based Non-autoregressive Speech Translation
Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu