241 citations · 258 across the 10 of their papers we have counts for
11 papers
Investigating Decoder-only Large Language Models for Speech-to-text Translation
Chao-Wei Huang, Hui Lu, Hongyu Gong +4
Large language models (LLMs), known for their exceptional reasoning capabilities, generalizability, and fluency across diverse domains, present a promising avenue for enhancing spe…
Textless Acoustic Model with Self-Supervised Distillation for Noise-Robust Expressive Speech-to-Speech Translation
Min-Jae Hwang, Ilia Kulikov, Benjamin Peloquin +3
In this paper, we propose a textless acoustic model with a self-supervised distillation strategy for noise-robust expressive speech-to-speech translation (S2ST). Recently proposed…
Improving Speech-to-Speech Translation Through Unlabeled Text
Xuan-Phi Nguyen, Sravya Popuri, Changhan Wang +3
Direct speech-to-speech translation (S2ST) is among the most challenging problems in the translation paradigm due to the significant scarcity of S2ST data. While effort has been ma…
Simple and Effective Unsupervised Speech Translation
Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen +5
The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled d…
Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models
Felix Stahlberg, Ilia Kulikov, Shankar Kumar
In many natural language processing (NLP) tasks the same input (e.g. source sentence) can have multiple possible outputs (e.g. translations). To analyze how this ambiguity (also kn…
Mode recovery in neural autoregressive sequence modeling
Ilia Kulikov, Sean Welleck, Kyunghyun Cho
Despite its wide use, recent studies have revealed unexpected and undesirable properties of neural autoregressive sequence models trained with maximum likelihood, such as an unreas…