Papers with USM
LAraBench: Benchmarking Arabic AI with Large Language Models (2024.eacl-long)
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Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. |
| Approach: | They used GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 datasets. |
| Outcome: | The proposed model outperforms SOTA models in zero-shot learning, with a few exceptions. |
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues (2023.acl-long)
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| Challenge: | Existing studies on USM neglect explicitly modeling the user’s task goals fulfillment using the task schema. |
| Approach: | They propose a schema-guided user satisfaction modeling framework that explicitly models the degree to which the user’s preferences regarding task attributes are fulfilled by the system. |
| Outcome: | The proposed framework outperforms existing methods on benchmark datasets and shows that it can interpret and scale well with unseen tasks and can work in low-resource settings. |
Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)
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Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar
| Challenge: | Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference. |
| Approach: | They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. |
| Outcome: | The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains. |
LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors (2025.findings-emnlp)
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| Challenge: | Recent studies show that pre-trained speech encoders and large language models can perform suboptimal performance on a range of spoken language processing tasks. |
| Approach: | They propose to combine large-scale pre-trained speech encoders and large-language models for better performance on automatic speech recognition tasks. |
| Outcome: | The proposed model can get an average of 49% WER reduction over the baseline model on 8 MLS testsets. |