1 citations · 1 across the 8 of their papers we have counts for
7 papers · 1 filter
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…
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…