Variational Low-Rank Adaptation for Personalized Impaired Speech Recognition
Abstract
Speech impairments resulting from congenital disorders, such as cerebral palsy, down syndrome, or apert syndrome, as well as acquired brain injuries due to stroke, traumatic accidents, or tumors, present major challenges to automatic speech recognition (ASR) systems. Despite recent advancements, state-of-the-art ASR models like Whisper still struggle with non-normative speech due to limited training data availability and high acoustic variability. Moreover, collecting and annotating non-normative speech is burdensome: speaking is effortful for many affected individuals, while laborious annotation often requires caregivers familiar with the speaker. This work introduces a novel ASR personalization method based on Bayesian Low-rank Adaptation for data-efficient fine-tuning. We validate our method on the English UA-Speech dataset and a newly collected German speech dataset, BF-Sprache, from a child with structural speech impairment. The dataset and approach are designed to reflect the challenges of low-resource settings that include individuals with speech impairments. Our method significantly improves ASR accuracy for impaired speech while maintaining data and annotation efficiency, offering a practical path toward 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 Bayesian Low-rank Adaptation framework for personalized impaired speech recognition, significantly improving ASR accuracy while addressing the challenges of data scarcity and variability in non-normative speech. The methodology and results contribute meaningfully to the field, offering practical solutions for inclusive communication technologies.
Methodology
The proposed methodology introduces a novel Bayesian Low-rank Adaptation (VI LoRA) framework, which effectively addresses the challenges of data scarcity and high variability in impaired speech recognition. The incorporation of variational inference to estimate the posterior distributions of adaptation parameters is a significant advancement over traditional low-rank adaptation methods. The dual prior approach for layer-wise weight variations is particularly innovative, allowing for a more informed adaptation process. However, the assumption of independence in the factorization of the variational parameters may limit the model's ability to capture complex interactions between layers.
Results & evaluation
The experiments are well-structured, utilizing two distinct datasets (UA-Speech and BF-Sprache) that highlight the effectiveness of the proposed method across different languages and intelligibility levels. The comparative analysis against various baselines, including full fine-tuning and standard LoRA, demonstrates the robustness and efficiency of VI LoRA, particularly in low-data scenarios. The results indicate substantial improvements in word and character error rates, especially for speakers with very low intelligibility, underscoring the practical applicability of the method.
Reproducibility
While the paper provides a detailed description of the methodology and experimental setup, it lacks specific implementation details that would facilitate reproducibility. The absence of a publicly available code repository or demo URL further hinders the ability of other researchers to replicate the findings. Clearer documentation of hyperparameters, training procedures, and data preprocessing steps would enhance reproducibility.
Limitations
The study acknowledges limitations related to the small speaker pool in the BF-Sprache dataset, which may affect the generalizability of the findings. Additionally, the reliance on contextual pattern matching in existing ASR systems could hinder language learning for children with speech impairments. The assumption of independent factorization in the variational parameters may not fully capture the complexities of the model, potentially impacting performance.
Broader impact
The proposed framework has significant implications for the development of inclusive ASR systems that can accommodate individuals with speech impairments. By improving recognition accuracy and maintaining data efficiency, the method can enhance communication for affected individuals, fostering social inclusion and educational opportunities. The approach also opens avenues for further research in low-resource speech recognition across languages, contributing to the broader field of assistive technologies. This paper presents a novel Bayesian Low-rank Adaptation framework for personalized impaired speech recognition, significantly improving ASR accuracy while addressing the challenges of data scarcity and variability in non-normative speech. The methodology and results contribute meaningfully to the field, offering practical solutions for inclusive communication technologies.
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