Finding My Voice: Generative Reconstruction of Disordered Speech for Automated Clinical Evaluation
Abstract
We present ChiReSSD, a speech reconstruction framework that preserves children speaker's identity while suppressing mispronunciations. Unlike prior approaches trained on healthy adult speech, ChiReSSD adapts to the voices of children with speech sound disorders (SSD), with particular emphasis on pitch and prosody. We evaluate our method on the STAR dataset and report substantial improvements in lexical accuracy and speaker identity preservation. Furthermore, we automatically predict the phonetic content in the original and reconstructed pairs, where the proportion of corrected consonants is comparable to the percentage of correct consonants (PCC), a clinical speech assessment metric. Our experiments show Pearson correlation of 0.63 between automatic and human expert annotations, highlighting the potential to reduce the manual transcription burden. In addition, experiments on the TORGO dataset demonstrate effective generalization for reconstructing adult dysarthric speech. Our results indicate that disentangled, style-based TTS reconstruction can provide identity-preserving speech across diverse clinical 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 ChiReSSD, a novel speech reconstruction framework that effectively addresses the unique challenges of disordered speech in children while preserving speaker identity. This work represents a meaningful advancement in the intersection of machine learning and clinical speech pathology, with the potential to significantly impact both research and practical applications in the field.
Methodology
The methodology presented in this paper is innovative, leveraging a modified version of StyleTTS2 to specifically address the challenges of reconstructing speech for children with speech sound disorders (SSD). The framework's ability to disentangle acoustic and prosodic features while preserving speaker identity is a significant advancement over traditional methods that often fail to account for the unique characteristics of children's speech. The adaptation of the model to handle the higher pitch and prosodic patterns of child speech is well-justified and effectively executed. However, the paper could benefit from a more detailed description of the training process and hyperparameter tuning, as these are critical for replicating the results.
Results & evaluation
The experimental evaluation is robust, utilizing multiple datasets (STAR, UltraSuite, and TORGO) to demonstrate the effectiveness of ChiReSSD across different populations. The results show substantial improvements in lexical accuracy and speaker identity preservation, with clear metrics such as WER, CER, and PCC providing quantitative support for the claims made. The correlation of automatic evaluations with human expert annotations (Pearson correlation of 0.63) is particularly noteworthy, as it suggests a practical application for reducing manual transcription efforts in clinical settings. The experiments are well-structured, but the paper could enhance clarity by providing more context for the choice of evaluation metrics.
Reproducibility
While the paper provides a general overview of the methods and datasets used, it lacks specific implementation details that would aid in reproducibility. For instance, the exact configurations of the model training, including learning rates, batch sizes, and the specific architecture of the StyleTTS2 modifications, are not thoroughly detailed. Including a supplementary material section with code snippets or a link to a repository would greatly enhance reproducibility.
Limitations
One limitation of the study is the reliance on specific datasets that may not fully represent the diversity of speech disorders in children. The generalization to adult dysarthric speech is promising, but the paper does not address potential limitations in applying the model to other populations or languages. Additionally, while the model shows improvements in phonetic accuracy, residual errors remain, and the paper suggests future work to address these, indicating that the current version may not be fully optimized.
Broader impact
The implications of this research are significant, particularly in the fields of speech-language pathology and assistive technology. By providing a framework that can improve the intelligibility of disordered speech while preserving the speaker's identity, ChiReSSD has the potential to enhance communication for children with SSD, thereby improving their social and academic outcomes. Furthermore, the ability to automate clinical evaluations could alleviate some of the burdens on speech-language therapists, allowing them to focus on more complex cases. The main contribution of this paper is the introduction of ChiReSSD, a novel speech reconstruction framework that effectively addresses the unique challenges of disordered speech in children while preserving speaker identity. This work represents a meaningful advancement in the intersection of machine learning and clinical speech pathology, with the potential to significantly impact both research and practical applications in the field.
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