Automatic Speech Recognition

Automatic speech recognition (ASR) studies how to turn audio into written words, supporting tasks such as transcription, captioning, voice interfaces, and clinical documentation. Systems commonly use self-supervised speech encoders with CTC or transducer decoders, encoder-decoder models, and increasingly speech-language models; adaptation, contextual biasing, and synthetic or unlabeled data help handle specialized vocabulary and underrepresented languages. Work also targets streaming and long-form audio, overlapping speakers, noisy recordings, and audiovisual cues, with evaluation on datasets such as LibriSpeech, Common Voice, and meeting and domain-specific benchmarks. Strong pretrained models coexist with persistent gaps in low-resource and atypical speech, dialect fairness, hallucination control, and real-world robustness, while word error rate alone often misses meaning, usability, and downstream impact.