12 citations · 25 across the 11 of their papers we have counts for
4 papers · 1 filter
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
Hsuan Su, Ting-Yao Hu, Hema Swetha Koppula +5
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data…
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Karren Yang, Ting-Yao Hu, Jen-Hao Rick Chang +2
Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount…
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
Ting-Yao Hu, Mohammadreza Armandpour, Ashish Shrivastava +3
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine learning with synthe…
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis
Ting-Yao Hu, Ashish Shrivastava, Oncel Tuzel +1
We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i.e., no style annotation, suc…