8 citations · 9 across the 4 of their papers we have counts for
4 papers
Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
Samuele Cornell, Jordan Darefsky, Zhiyao Duan +1
Currently, a common approach in many speech processing tasks is to leverage large scale pre-trained models by fine-tuning them on in-domain data for a particular application. Yet o…
Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation
Yinghao Aaron Li, Xilin Jiang, Jordan Darefsky +2
The rapid advancement of large language models (LLMs) has significantly propelled the development of text-based chatbots, demonstrating their capability to engage in coherent and c…
Cacophony: An Improved Contrastive Audio-Text Model
Ge Zhu, Jordan Darefsky, Zhiyao Duan
Despite recent advancements, audio-text models still lag behind their image-text counterparts in scale and performance. In this paper, we propose to improve both the data scale and…
Music Source Separation with Generative Flow
Ge Zhu, Jordan Darefsky, Fei Jiang +2
Fully-supervised models for source separation are trained on parallel mixture-source data and are currently state-of-the-art. However, such parallel data is often difficult to obta…