EvolveCaptions: Empowering DHH Users Through Real-Time Collaborative Captioning
Abstract
Automatic Speech Recognition (ASR) systems often fail to accurately transcribe speech from Deaf and Hard of Hearing (DHH) individuals, especially during real-time conversations. Existing personalization approaches typically require extensive pre-recorded data and place the burden of adaptation on the DHH speaker. We present EvolveCaptions, a real-time, collaborative ASR adaptation system that supports in-situ personalization with minimal effort. Hearing participants correct ASR errors during live conversations. Based on these corrections, the system generates short, phonetically targeted prompts for the DHH speaker to record, which are then used to fine-tune the ASR model. In a study with 12 DHH and six hearing participants, EvolveCaptions reduced Word Error Rate (WER) across all DHH users within one hour of use, using only five minutes of recording time on average. Participants described the system as intuitive, low-effort, and well-integrated into communication. These findings demonstrate the promise of collaborative, real-time ASR adaptation for more equitable 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
EvolveCaptions represents a meaningful advancement in the field of machine learning for accessibility, combining innovative ASR adaptation techniques with real-time user collaboration to empower Deaf and Hard of Hearing individuals. The paper's contributions to both methodology and social impact are commendable, although further work is needed to enhance reproducibility and address limitations.
Methodology
The methodology employed in EvolveCaptions is innovative as it combines real-time collaborative error correction with a personalized ASR adaptation process. The approach of using hearing participants to correct ASR outputs and subsequently generating targeted prompts for the DHH speaker is a novel way to facilitate in-situ personalization. This method minimizes the burden on DHH users while actively engaging hearing participants, which is a significant departure from traditional ASR personalization methods that require extensive pre-recorded data. The integration of the Whisper ASR model for fine-tuning based on user corrections is also a strong aspect of the methodology.
Results & evaluation
The experimental evaluation is robust, involving a diverse group of participants (12 DHH and 6 hearing individuals) and demonstrating a clear reduction in Word Error Rate (WER) across all DHH users within a short time frame. The use of only five minutes of recording time to achieve significant improvements in ASR accuracy is particularly noteworthy. However, the sample size is relatively small, which may limit the generalizability of the findings.
Reproducibility
The paper lacks detailed implementation specifics that would aid in reproducibility. While the overall concept is clear, additional information on the ASR model fine-tuning process, data collection methods, and system architecture would be beneficial for other researchers looking to replicate or build upon this work.
Limitations
One limitation identified is the small participant pool, which may not capture the full diversity of DHH users and their communication styles. Additionally, the reliance on hearing participants for corrections may introduce biases in the ASR adaptation process. The paper does not address potential challenges in environments with multiple speakers or varying levels of background noise, which could affect ASR performance.
Broader impact
The broader impact of EvolveCaptions is significant, as it addresses a critical gap in accessibility for DHH individuals in real-time communication settings. By facilitating more equitable communication through collaborative ASR adaptation, this work has the potential to improve social interactions, educational experiences, and professional opportunities for DHH users. The approach could also inspire further research into collaborative technologies that enhance accessibility across various domains. EvolveCaptions represents a meaningful advancement in the field of machine learning for accessibility, combining innovative ASR adaptation techniques with real-time user collaboration to empower Deaf and Hard of Hearing individuals. The paper's contributions to both methodology and social impact are commendable, although further work is needed to enhance reproducibility and address limitations.
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