Methods To Dysfluent Speech Transcription And Detection
Tech ID: 33377 / UC Case 2024-062-0
Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level. However, current research in dysfluency modeling primarily focuses on either transcription or detection, and the performance of each aspect remains limited.
To address this problem, UC Berkeley researchers have developed a new unconstrained dysfluency modeling (UDM) approach that addresses both transcription and detection in an automatic and hierarchical manner. Furthermore, a simulated dysfluent dataset called VCTK++ enhances the capabilities of UDM in phonetic transcription. The effectiveness and robustness of UDM in both transcription and detection tasks has been demonstrated experimentally.
UDM eliminates the need for extensive manual annotation by providing a comprehensive solution.
- Comprehensive solution
- Automated, hierarchical approach
- Demonstrated effectiveness
- Diagnosis of speech disorders in, e.g., hospitals
- Evaluation of early language literacy is school settings
- Speech and Language Pathology