SpeechAgent: An End-to-End Mobile Infrastructure for Speech Impairment Assistance
Abstract
Speech is essential for human communication, yet millions of people face impairments such as dysarthria, stuttering, and aphasia conditions that often lead to social isolation and reduced participation. Despite recent progress in automatic speech recognition (ASR) and text-to-speech (TTS) technologies, accessible web and mobile infrastructures for users with impaired speech remain limited, hindering the practical adoption of these advances in daily communication. To bridge this gap, we present SpeechAgent, a mobile SpeechAgent designed to facilitate people with speech impairments in everyday communication. The system integrates large language model (LLM)- driven reasoning with advanced speech processing modules, providing adaptive support tailored to diverse impairment types. To ensure real-world practicality, we develop a structured deployment pipeline that enables real-time speech processing on mobile and edge devices, achieving imperceptible latency while maintaining high accuracy and speech quality. Evaluation on real-world impaired speech datasets and edge-device latency profiling confirms that SpeechAgent delivers both effective and user-friendly performance, demonstrating its feasibility for personalized, day-to-day assistive communication.
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 development of SpeechAgent, a mobile infrastructure that leverages advanced speech processing and LLMs to assist individuals with speech impairments in real-time communication. This work represents a meaningful advancement in the field of assistive technology, addressing a critical need for effective communication tools for individuals with diverse speech disorders.
Methodology
The methodology presented in the paper is comprehensive and innovative, integrating multiple advanced techniques such as large language models (LLMs) for reasoning, automatic speech recognition (ASR) for transcription, and text-to-speech (TTS) for output generation. The structured workflow effectively combines these components to refine impaired speech in real-time, which is a significant advancement over traditional approaches that often lack adaptability and real-time capabilities. The use of a client-server architecture ensures that computationally intensive tasks are handled efficiently, making the system practical for mobile deployment.
Results & evaluation
The experimental evaluation is robust, utilizing a diverse set of datasets that encompass various types of speech impairments. The authors provide detailed performance metrics, including accuracy, clarity, and user satisfaction, which are crucial for assessing the system's effectiveness. The results demonstrate significant improvements in clarity and usability compared to baseline systems, indicating that the SpeechAgent effectively meets its intended goals. However, the paper could benefit from more extensive user studies to validate the system's performance in real-world scenarios.
Reproducibility
The paper mentions the release of code and datasets, which is a positive step towards reproducibility. However, the details regarding the implementation of the models, hyperparameters, and training procedures are somewhat limited, which may hinder full reproducibility by other researchers. Clearer documentation and access to the training process would enhance the reproducibility of the results.
Limitations
One limitation noted in the paper is the potential for occasional hallucinations from the language model, which can lead to inaccuracies in the refined speech output. Additionally, while the system shows promise for various speech impairments, its performance across different languages and dialects has not been thoroughly explored, which may limit its applicability in diverse populations.
Broader impact
The SpeechAgent has significant potential implications for improving communication for individuals with speech impairments, thereby enhancing their social participation and quality of life. By enabling clearer communication, the system could foster greater inclusion and accessibility in various settings, including education and employment. The approach taken in this paper could also inspire further research into assistive technologies for other forms of disabilities. The main contribution of this paper is the development of SpeechAgent, a mobile infrastructure that leverages advanced speech processing and LLMs to assist individuals with speech impairments in real-time communication. This work represents a meaningful advancement in the field of assistive technology, addressing a critical need for effective communication tools for individuals with diverse speech disorders.
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