113 citations · 278 across the 21 of their papers we have counts for
4 papers · 1 filter
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition
Zhong Meng, Weiran Wang, Rohit Prabhavalkar +7
We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves ra…
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
Alëna Aksënova, Zhehuai Chen, Chung-Cheng Chiu +8
Building inclusive speech recognition systems is a crucial step towards developing technologies that speakers of all language varieties can use. Therefore, ASR systems must work fo…
A Scalable Model Specialization Framework for Training and Inference using Submodels and its Application to Speech Model Personalization
Fadi Biadsy, Youzheng Chen, Xia Zhang +3
Model fine-tuning and adaptation have become a common approach for model specialization for downstream tasks or domains. Fine-tuning the entire model or a subset of the parameters…
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior
Guangzhi Sun, Yu Zhang, Ron J. Weiss +5
Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech. Such models typically incorporate a fine-gr…