Speech Recognition Performance

Automatic speech recognition (ASR) research aims to improve the accuracy and robustness of systems that convert spoken language into text. Current efforts focus on addressing challenges like noisy environments, dialectal variations (including those stemming from recording quality), and low-resource languages, often employing techniques like advanced clustering, language modeling, and self-supervised learning within architectures such as Wav2Vec, Conformers, and transformer-based models. These advancements have significant implications for various applications, from improving accessibility for individuals with speech impairments to enabling more efficient and equitable use of voice-activated technologies across diverse populations. Furthermore, research is actively exploring methods to improve fairness and reduce bias in ASR systems.