1 citations · 1 across the 8 of their papers we have counts for
8 papers
Can Speech LLMs Think while Listening?
Yi-Jen Shih, Desh Raj, Chunyang Wu +6
Recent advances in speech large language models (speech LLMs) have enabled seamless spoken interactions, but these systems still struggle with complex reasoning tasks. Previously,…
Transducer-Llama: Integrating LLMs into Streamable Transducer-based Speech Recognition
Keqi Deng, Jinxi Guo, Yingyi Ma +4
While large language models (LLMs) have been applied to automatic speech recognition (ASR), the task of making the model streamable remains a challenge. This paper proposes a novel…
When Every Token Counts: Optimal Segmentation for Low-Resource Language Models
Bharath Raj, Garvit Suri, Vikrant Dewangan +1
Traditional greedy tokenization methods have been a critical step in Natural Language Processing (NLP), influencing how text is converted into tokens and directly impacting model p…
Get Large Language Models Ready to Speak: A Late-fusion Approach for Speech Generation
Maohao Shen, Shun Zhang, Jilong Wu +5
Large language models (LLMs) have revolutionized natural language processing (NLP) with impressive performance across various text-based tasks. However, the extension of text-domin…
Effective internal language model training and fusion for factorized transducer model
Jinxi Guo, Niko Moritz, Yingyi Ma +6
The internal language model (ILM) of the neural transducer has been widely studied. In most prior work, it is mainly used for estimating the ILM score and is subsequently subtracte…
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs
Yassir Fathullah, Chunyang Wu, Egor Lakomkin +7
In this work, we extend the instruction-tuned Llama-2 model with end-to-end general-purpose speech processing and reasoning abilities while maintaining the wide range of original L…
Augmenting text for spoken language understanding with Large Language Models
Roshan Sharma, Suyoun Kim, Daniel Lazar +7
Spoken semantic parsing (SSP) involves generating machine-comprehensible parses from input speech. Training robust models for existing application domains represented in training d…
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models
Yuan Shangguan, Haichuan Yang, Danni Li +11
Automatic Speech Recognition (ASR) models need to be optimized for specific hardware before they can be deployed on devices. This can be done by tuning the model's hyperparameters…