Cross-lingual Evaluation Of Hypernasality Using Wav2Vec2 Features

Abstract

Hypernasality, a speech resonance disorder characterized by excessive nasal airflow, presents challenges in accurate detection across languages. Traditional assessments of hypernasality include perceptual evaluation and nasometry. More recently, objective acoustic measures based on formant analysis and other acoustic features have been proposed as proxies for hypernasality; however, these acoustic measures exhibit considerable variability, especially in multilingual contexts. In this study, we utilize the wav2vec2-large-xlsr-53 model, a cross-lingual speech representation framework, and evaluate hypernasality-related features within its transformer layers. We extracted features from each layer of the model’s transformer and trained a machine learning model to predict hypernasality ratings across three datasets: Americleft (English), New Mexico Cleft Palate Center (English), and All India Institute of Speech and Hearing (Kannada). Our analysis reveals that the 11th and 12th layer contextualized embeddings effectively model hypernasality cross-lingually, demonstrating significant within-language average correlations (0.78 and 0.78) and cross-lingual average correlations (0.68 and 0.67) between predicted and perceptual ratings. These findings suggest that the wav2vec2-large-xlsr-53 model’s intermediate layers effectively capture hypernasality cross-lingually.