ASR Disfluent Speech Recognition via Dynamic Model Adaptation
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Solution Overview
Problem
Automatic speech recognition systems struggle to accurately interpret disfluent speech, which includes breaks, irregularities, and non-lexical sounds, often due to conditions like stuttering or nasality, leading to poor recognition performance.
Innovation Solution
The system determines the presence and type of disfluent speech and compensates by using optimized statistical language models and acoustic models specific to disfluent speech types, modifying ASR variables to improve recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the ASR system uses standard speech recognition models designed for fluent speech, then the system structure remains simple and easy to implement, but the recognition accuracy deteriorates when processing disfluent speech
Solution Approach 1:
The ASR system dynamically adapts its processing parameters and model selection based on the detected disfluency characteristics. The system adjusts acoustic model parameters, grammar rules, and processing thresholds in real-time according to the speaker's speech patterns, enabling accurate recognition of disfluent speech without requiring a completely separate complex system.
Solution Approach 2:
The system modifies key ASR parameters such as acoustic model variance, language model weighting, and decision thresholds based on detected disfluency types. By changing these parameters dynamically, the system optimizes recognition accuracy for different speech patterns without fundamentally altering the core system architecture.
2Reliability
If the ASR system is programmed with assumptions about fluent speech patterns, then the programming and implementation become straightforward, but the system fails to accurately interpret speech from people with speech difficulties
Solution Approach 1:
The system segments speech processing into distinct phases: initial fluent speech processing using standard models, disfluency detection phase, and adapted processing phase. This segmentation allows the system to maintain simple fluent speech handling while adding specialized处理能力 for disfluent speech without compromising overall reliability.
Solution Approach 2:
The system performs preliminary analysis of speech patterns to detect disfluency characteristics before main recognition processing. By identifying speech difficulties early in the processing pipeline, the system can pre-adjust parameters and select appropriate models, ensuring accurate interpretation from the outset rather than attempting to correct errors later.
3Measurement precision
If the ASR system uses a single processing model for all speech types, then the system complexity remains low, but the recognition performance deteriorates for specific disfluent speech types such as stuttering or nasality
Solution Approach 1:
The ASR system implements a universal framework that can handle both fluent and disfluent speech through a single integrated architecture. The system selectively applies different processing strategies and parameter sets within this unified framework, allowing it to maintain low overall complexity while achieving high accuracy across diverse speech types including stuttering, stammering, and nasality.
Data Source
AI summary
A system and method of processing disfluent speech at an automatic speech recognition (ASR) system includes: receiving speech from a speaker via a microphone; determining the received speech includes disfluent speech; accessing a disfluent speech grammar or acoustic model in response to the determination; and processing the received speech using the disfluent speech grammar.