Wav2Vec2 Model

Wav2Vec2 is a self-supervised learning model for speech representation, primarily used as a powerful feature extractor for various downstream tasks in speech processing. Current research focuses on improving its performance in low-resource languages, detecting pathological speech and mispronunciations, and enhancing its robustness to noise and accents, often employing techniques like low-rank adaptation and multiview canonical correlation analysis. This model's ability to learn rich, high-level speech features has significant implications for applications ranging from automatic speech recognition and speaker identification to clinical speech analysis and the creation of more inclusive and accessible speech technologies.