Speech Recognition Task

Speech recognition (ASR) research focuses on developing systems that accurately transcribe spoken language into text. Current efforts concentrate on improving model robustness across diverse acoustic conditions and languages, often leveraging self-supervised learning with architectures like Conformers and Wav2vec 2.0, and exploring techniques like model compression (quantization and pruning) and efficient transfer learning (adapters) to reduce computational costs and improve scalability. These advancements are crucial for deploying ASR systems on resource-constrained devices and expanding their applicability to low-resource languages and noisy environments, impacting fields ranging from virtual assistants to accessibility technologies.