5 citations · 7 across the 4 of their papers we have counts for
5 papers
Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessment
Ke Wang, Wenning Wei, Yan Deng +2
Automatic Pronunciation Assessment (APA) is critical for Computer-Assisted Language Learning (CALL), requiring evaluation across multiple granularities and aspects. Large Multimoda…
Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment
Ke Wang, Lei He, Kun Liu +3
Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with…
MuLanTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2023
Zhihang Xu, Shaofei Zhang, Xi Wang +4
In this paper, we present MuLanTTS, the Microsoft end-to-end neural text-to-speech (TTS) system designed for the Blizzard Challenge 2023. About 50 hours of audiobook corpus for Fre…
On Addressing Practical Challenges for RNN-Transducer
Rui Zhao, Jian Xue, Jinyu Li +3
In this paper, several works are proposed to address practical challenges for deploying RNN Transducer (RNN-T) based speech recognition system. These challenges are adapting a well…
Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capability
Jinyu Li, Rui Zhao, Zhong Meng +8
Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic spee…