Disfluent Speech Transcription and Detection via 2D Alignment
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Solution Overview
Problem
Current AI tools are inadequate for automating the detection and transcription of disfluent speech, which is characterized by abnormal patterns such as repetition, replacement, and irregular pauses, and existing ASR systems fail to accurately recognize these disfluencies at both word and phoneme levels, leading to inefficiencies in speech therapy and language learning.
Innovation Solution
The Hierarchical Unconstrained Disfluency Modeling (H-UDM) framework integrates transcription and detection modules within a unified framework, employing a Unconstrained Recursive Forced Aligner (URFA) with dynamic alignment search and 2D alignment to detect various disfluency types, including replacements, insertions, deletions, and repetitions, and incorporates a Connectionist Temporal Classification (CTC) module for enhanced non-monotonicity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If current ASR systems are used for speech transcription, then general speech recognition is achieved, but disfluency detection accuracy deteriorates
Solution Approach 1:
The system segments the speech processing task into multiple levels: phoneme-level alignment, word-level transcription, and disfluency-level detection. This hierarchical segmentation allows each component to specialize in detecting specific disfluency patterns at appropriate granularities, resolving the contradiction between automation and precision.
Solution Approach 2:
The system transitions from traditional 1D sequential processing to 2D alignment space, where one dimension represents speech time and the other represents text time. This dimensional transformation enables simultaneous modeling of temporal dynamics and disfluency patterns, achieving both automation and precision.
2Speed
If end-to-end methods are used for stuttering detection, then detection speed is improved, but handling of other disfluency types deteriorates
Solution Approach 1:
The system designs a universal disfluency detection framework that can handle multiple disfluency types (stuttering, repetitions, corrections, fillers) through a single unified model. The 2D alignment-based approach naturally captures various disfluency patterns without requiring separate specialized models, achieving both speed and versatility.
3Measurement precision
If manual labeling is performed for disfluency detection, then detection accuracy is improved, but scalability deteriorates
Solution Approach 1:
The system enables semi-automated annotation where the 2D alignment model automatically generates disfluency labels based on alignment discrepancies, requiring minimal manual intervention. This self-service capability maintains high detection accuracy while dramatically improving scalability and reducing annotation costs.
4Ease of operation
If word-level ASR is used, then transcription simplicity is improved, but phoneme-level disfluency detection deteriorates
Solution Approach 1:
The system segments the transcription and detection processes into phoneme-level alignment (for detecting phoneme-level disfluencies) and word-level transcription (for overall simplicity). This multi-level segmentation allows the system to maintain ease of operation while achieving phoneme-level detection precision.
Data Source
AI summary
A method for processing audio inputs to detect and transcribe disfluencies includes: receiving an audio input comprising spoken language; generating a phonetic transcription of the audio input by applying a recursive forced alignment process that produces a two-dimensional alignment without reliance on a monotonic alignment constraint; identifying disfluencies within the audio input by comparing a pre-determined number of disfluency templates to the two-dimensional alignment; providing a timestamp for each detected disfluency; and outputting a transcription of the audio input that includes indications of the detected disfluencies and their respective timestamps.