4 citations · 14 across the 11 of their papers we have counts for
18 papers
CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models
Blake Olson, Yuhang Song, Emmett McQuinn +1
Diffusion Language Models (DLMs) offer promising parallel generation capabilities but lag behind autoregressive models in complex reasoning and tool-use tasks. While Reinforcement…
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…
Prompting Large Language Models with Speech Recognition Abilities
Yassir Fathullah, Chunyang Wu, Egor Lakomkin +9
Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. I…
Towards Selection of Text-to-speech Data to Augment ASR Training
Shuo Liu, Leda Sarı, Chunyang Wu +4
This paper presents a method for selecting appropriate synthetic speech samples from a given large text-to-speech (TTS) dataset as supplementary training data for an automatic spee…
Multi-Head State Space Model for Speech Recognition
Yassir Fathullah, Chunyang Wu, Yuan Shangguan +8
State space models (SSMs) have recently shown promising results on small-scale sequence and language modelling tasks, rivalling and outperforming many attention-based approaches. I…
Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches
Vinicius Ribeiro, Yiteng Huang, Yuan Shangguan +3
Wake word detection exists in most intelligent homes and portable devices. It offers these devices the ability to "wake up" when summoned at a low cost of power and computing. This…