Audioneme - Detecting Speech Disorder in Children

Auditory perceptual analysis (APA) is the main method for clinical assessment of speech-language deficits, which are one of the most prevalent childhood disabilities. However, results from APA are susceptible to intra- and inter-rater variability. In addition, there are other limitations of manual or hand transcription-based speech disorder diagnostic methods. To address these limitations, there is increased interest in developing automated methods that quantify speech patterns for diagnosing speech disorders in children. In this project, I fine-tuned Facebook’s Wav2Vec2 on child speech data in conjunction with the utterance transcriptions to automate screening and assessment of speech disorders and speech intelligibility in children. The dataset for this project consisted of weakly labeled utterances comprising ~15,000 recordings of children with and without speech disorder.

The GitHub repository for this work can be found here: https://github.com/keshavbhandari/Audioneme