Year: 2024
Type: conference-paper
Abstract: Mispronunciation Detection and Diagnosis (MDD) systems, leveraging Automatic Speech Recognition (ASR), face two main challenges in Mandarin Chinese: 1) The two-stage models create an information gap between the phoneme or tone classification stage and the MDD stage.2) The scarcity of Mandarin MDD datasets limits model training.In this paper, we introduce a stateless RNN-T model for Mandarin MDD, utilizing HuBERT features with pitch embedding through a Pitch Fusion Block.Our model, trained solely on native speaker data, shows a 3% improvement in Phone Error Rate and a 7% increase in False Acceptance Rate over the state-of-the-art baseline in nonnative scenarios.
Source: Interspeech (USB)
Authors: Xintong Wang, Mingqian Shi, Ye Wang
Institution: National University of Singapore
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: Ministry of Education, India
OpenAlex ID: W4402111668