Idiosyncratic Versus Normative Modeling of Atypical Speech Recognition: Dysarthric Case Studies
Abstract
State-of-the-art automatic speech recognition (ASR) models like Whisper, perform poorly on atypical speech, such as that produced by individuals with dysarthria. Past works for atypical speech have mostly investigated fully personalized (or idiosyncratic) models, but modeling strategies that can both generalize and handle idiosyncracy could be more effective for capturing atypical speech. To investigate this, we compare four strategies: (a) $\textit{normative}$ models trained on typical speech (no personalization), (b) $\textit{idiosyncratic}$ models completely personalized to individuals, (c) $\textit{dysarthric-normative}$ models trained on other dysarthric speakers, and (d) $\textit{dysarthric-idiosyncratic}$ models which combine strategies by first modeling normative patterns before adapting to individual speech. In this case study, we find the dysarthric-idiosyncratic model performs better than idiosyncratic approach while requiring less than half as much personalized data (36.43 WER with 128 train size vs 36.99 with 256). Further, we found that tuning the speech encoder alone (as opposed to the LM decoder) yielded the best results reducing word error rate from 71% to 32% on average. Our findings highlight the value of leveraging both normative (cross-speaker) and idiosyncratic (speaker-specific) patterns to improve ASR for underrepresented speech populations.
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
The main contribution of this paper is the introduction of a dysarthric-idiosyncratic modeling approach that effectively combines normative and personalized strategies to enhance ASR performance for individuals with dysarthria. This work not only advances the technical understanding of ASR in atypical speech contexts but also highlights the need for more inclusive and representative datasets in machine learning research.
Methodology
The paper presents a systematic comparison of four modeling strategies for automatic speech recognition (ASR) tailored to dysarthric speech, which is a significant contribution to the field. The methodology is well-structured, employing a combination of normative and idiosyncratic modeling approaches. The use of transfer learning and fine-tuning techniques is appropriate given the limited data available for dysarthric speakers. However, while the methodology is sound, it could benefit from a more detailed explanation of the parameter-efficient strategies employed and their specific impacts on model performance.
Results & evaluation
The experiments are thorough, utilizing a well-defined dataset (TORGO) and employing leave-one-out cross-validation to ensure robustness. The results demonstrate clear improvements in word error rates (WER) across different modeling strategies, particularly highlighting the effectiveness of the dysarthric-idiosyncratic model. However, the reliance on a small dataset limits the generalizability of the findings, and the paper could have included more extensive comparisons with existing state-of-the-art models.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including model architecture, training parameters, and evaluation metrics, which supports reproducibility. The availability of the GitHub repository further enhances the potential for other researchers to replicate the study. However, a more detailed description of the data preprocessing steps would improve clarity.
Limitations
The study is limited by the small number of speakers in the TORGO dataset, which may not capture the full diversity of dysarthric speech. Additionally, the paper acknowledges that factors such as regional accents and dialects were not controlled, which could influence results. The authors also note the need for larger datasets to validate their findings, particularly in real-world applications.
Broader impact
This research has significant implications for improving ASR systems for individuals with dysarthria, a population often underserved by current technologies. By demonstrating that a hybrid approach can outperform purely personalized models, the findings could lead to more accessible and effective speech recognition tools in clinical settings. The study also emphasizes the importance of inclusive AI development, which is crucial for ensuring that technological advancements benefit all users, including those with disabilities. The main contribution of this paper is the introduction of a dysarthric-idiosyncratic modeling approach that effectively combines normative and personalized strategies to enhance ASR performance for individuals with dysarthria. This work not only advances the technical understanding of ASR in atypical speech contexts but also highlights the need for more inclusive and representative datasets in machine learning research.
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