Towards Hierarchical Spoken Language Dysfluency Modeling
CoRR(2024)
摘要
Speech dysfluency modeling is the bottleneck for both speech therapy and
language learning. However, there is no AI solution to systematically tackle
this problem. We first propose to define the concept of dysfluent speech and
dysfluent speech modeling. We then present Hierarchical Unconstrained
Dysfluency Modeling (H-UDM) approach that addresses both dysfluency
transcription and detection to eliminate the need for extensive manual
annotation. Furthermore, we introduce a simulated dysfluent dataset called
VCTK++ to enhance the capabilities of H-UDM in phonetic transcription. Our
experimental results demonstrate the effectiveness and robustness of our
proposed methods in both transcription and detection tasks.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要