Xing Niu

Dec 24, 2024 | amazon.science | Karel Mundnich |Xing Niu |Prashant Mathur |Srikanth Ronanki

Despite recent advancements in speech processing, zero-resource speech translation (ST) and automatic speech recognition (ASR) remain challenging problems. In this work, we propose to leverage a multilingual Large Language Model (LLM) to perform ST and ASR in languages for which the model has never seen paired audio-text data.

May 13, 2023 | amazon.science | Xing Niu |Georgiana Dinu |Prashant Mathur |Anna Currey

The training data used in NMT is rarely controlled with respect to specific attributes, such as word casing or gender, which can cause errors in translations. We argue that predicting the target word and attributes simultaneously is an effective way to ensure that translations are more faithful to the training data distribution with respect to these attributes.