no code implementations • 12 Sep 2023 • Arpita Vats, Zhe Liu, Peng Su, Debjyoti Paul, Yingyi Ma, Yutong Pang, Zeeshan Ahmed, Ozlem Kalinli
To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data.
no code implementations • 1 Sep 2023 • Chuanneng Sun, Zeeshan Ahmed, Yingyi Ma, Zhe Liu, Lucas Kabela, Yutong Pang, Ozlem Kalinli
We propose to leverage prompts for a LLM without fine tuning during rescoring which incorporate a biasing list and few-shot examples to serve as additional information when calculating the score for the hypothesis.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 20 Jan 2023 • Szu-Jui Chen, Debjyoti Paul, Yutong Pang, Peng Su, Xuedong Zhang
With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 20 Jul 2022 • Laxmi Pandey, Debjyoti Paul, Pooja Chitkara, Yutong Pang, Xuedong Zhang, Kjell Schubert, Mark Chou, Shu Liu, Yatharth Saraf
Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • 22 Nov 2019 • Yiren Wang, Hongzhao Huang, Zhe Liu, Yutong Pang, Yongqiang Wang, ChengXiang Zhai, Fuchun Peng
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference.