User profiles and auditory-perceptual evaluations upon the launch of the All-Voiced app for voice evaluation: Initial insights and training potential

Neus Calaf
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Abstract

Introduction

Auditory-perceptual evaluation is key for diagnosing voice disorders, but variability in judgments underscores the need for improved training materials. The All-Voiced app was developed to enhance consistency in evaluations through real-time feedback and data-driven training.

Objective

To present preliminary findings from the first 75 days following the launch of the latest version of the app (September 2024), focusing on user profiles and voice evaluations, exploring the app's potential for deeper insights as more data is gathered.

Method

The latest version of the All-Voiced app, launched in September 2024, includes a fully integrated backend for data collection from users who provide consent. The app enables users to practice voice evaluations, receive feedback, and contribute to research. Descriptive statistics were used to analyze user profiles and evaluations from the first 75 days post-launch of this latest version. Box plots and scatter plots were used to compare the evaluations of All-Voiced users with PVQD ratings (Walden, 2022) across different competence levels.

Results

A total of 264 participants registered in the app, with daily registration patterns showing consistent activity. Most participants were aged 21–30 (49%), identified with “she/her” pronouns (88%), and were from the United States (51%). The majority were speech-language pathologists (78%), and 40% were beginners in terms of competence level. A total of 557 evaluations were collected across 112 voice samples. Analysis of the three most frequently evaluated voice samples revealed distinct patterns of consensus among evaluators with varying levels of expertise, hinting at trends that could have significant implications as more data is collected.

Conclusion

The All-Voiced app has been well received, particularly by speech-language pathologists, highlighting its promise as a tool for auditory-perceptual training. By collecting and analyzing large-scale data, the app holds potential to address limitations in the subjective nature of evaluations, enhance reliability, and support evidence-based practices in voice disorders management.
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