  Speech disfluency modeling is the bottleneck for both speech therapy and
language learning. However, there is no effective AI solution to systematically
tackle this problem. We solidify the concept of disfluent speech and disfluent
speech modeling. We then present Hierarchical Unconstrained Disfluency Modeling
(H-UDM) approach, the hierarchical extension of UDM that addresses both
disfluency transcription and detection to eliminate the need for extensive
manual annotation. Our experimental findings serve as clear evidence of the
effectiveness and reliability of the methods we have introduced, encompassing
both transcription and detection tasks.
