Data-Efficient ASR Personalization for Non-Normative Speech Using an Uncertainty-Based Phoneme Difficulty Score for Guided Sampling
Abstract
Automatic speech recognition (ASR) systems struggle with non-normative speech from individuals with impairments caused by conditions like cerebral palsy or structural anomalies. The high acoustic variability and scarcity of training data severely degrade model performance. This work introduces a data-efficient personalization method that quantifies phoneme-level uncertainty to guide fine-tuning. We leverage Monte Carlo Dropout to estimate which phonemes a model finds most difficult and use these estimates for a targeted oversampling strategy. We validate our method on English and German datasets. Crucially, we demonstrate that our model-derived uncertainty strongly correlates with phonemes identified as challenging in an expert clinical logopedic report, marking, to our knowledge, the first work to successfully align model uncertainty with expert assessment of speech difficulty. Our results show that this clinically-validated, uncertainty-guided sampling significantly improves ASR accuracy, delivering a practical framework for personalized and inclusive ASR.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a novel framework for data-efficient ASR personalization that utilizes uncertainty-based phoneme difficulty scoring to improve recognition accuracy for non-normative speech. The integration of clinical validation with machine learning techniques represents a meaningful contribution to both the fields of speech recognition and assistive technology.
Methodology
The methodology is robust, leveraging Monte Carlo Dropout to quantify phoneme-level uncertainty, which is innovative in the context of ASR personalization for non-normative speech. The introduction of the Phoneme Difficulty Score (PhDScore) is a significant advancement, as it combines multiple uncertainty metrics to guide oversampling effectively. The approach to link model uncertainty with clinical assessments is particularly noteworthy and demonstrates a thoughtful integration of machine learning with clinical insights.
Results & evaluation
The experiments are well-structured, utilizing both English and German datasets to validate the proposed method. The results show a clear improvement in ASR accuracy for non-normative speech, and the correlation with clinical assessments adds credibility to the findings. However, the limited number of speakers in the BF-Sprache dataset may affect the generalizability of the results.
Reproducibility
While the paper provides a detailed description of the methodology, including the computation of the PhDScore and the experimental setup, it lacks specific implementation details or code availability, which could hinder reproducibility. Future work should consider sharing code or datasets to facilitate further research.
Limitations
The primary limitation is the small size of the BF-Sprache dataset, which restricts the breadth of the findings. Additionally, the subjective nature of clinical assessments may introduce variability in the validation process. The trade-off between personalization and generalization is also a concern, as it may limit the practical application of the method in real-world scenarios.
Broader impact
This work has significant implications for the development of personalized ASR systems, particularly for individuals with speech impairments. By improving the accuracy of ASR for non-normative speech, the proposed method could enhance communication aids and assistive technologies, making them more effective and inclusive for users with diverse speech patterns. This paper presents a novel framework for data-efficient ASR personalization that utilizes uncertainty-based phoneme difficulty scoring to improve recognition accuracy for non-normative speech. The integration of clinical validation with machine learning techniques represents a meaningful contribution to both the fields of speech recognition and assistive technology.
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