Jianwei Yu

Recurrent Neural Network Language Model Training Using Natural Gradient

2019

Recurrent neural network language models (RNNLMs) have become an increasing popular choice for state-of-the-art speech recognition systems. RNNLMs are normally trained by minimizing the cross entropy (CE) using the stochastic gradient descent (SGD) algorithm. However, …

Improved End-to-End Dysarthric Speech Recognition via Meta-learning Based Model Re-initialization

2021

Dysarthric speech recognition is a challenging task as dysarthric data is limited and its acoustics deviate significantly from normal speech. Model-based speaker adaptation is a promising method by using the limited dysarthric speech to fine-tune …

Improved Factorized Neural Transducer Model For text-only Domain Adaptation

2023 · arXiv (Cornell University)

Adapting End-to-End ASR models to out-of-domain datasets with text data is challenging. Factorized neural Transducer (FNT) aims to address this issue by introducing a separate vocabulary decoder to predict the vocabulary. Nonetheless, this approach has …

AutoPrep: An Automatic Preprocessing Framework for In-the-Wild Speech Data

2023 · arXiv (Cornell University)

Recently, the utilization of extensive open-sourced text data has significantly advanced the performance of text-based large language models (LLMs). However, the use of in-the-wild large-scale speech data in the speech technology community remains constrained. One …

Preference Alignment Improves Language Model-Based TTS

2025

Recent advancements in text-to-speech (TTS) have shown that language model (LM)-based systems offer competitive performance to their counterparts. Further optimization can be achieved through preference alignment algorithms, which adjust LMs to align with the preferences …