Speech recognition
Definition
Speech recognition (ASR) transcribes audio into text. Related areas include speaker identification, speech synthesis (TTS), and spoken language understanding.
It bridges multimodal (audio as one modality) and NLP (output is text). Modern ASR is mostly end-to-end neural; self-supervised pretraining (e.g. wav2vec 2.0) reduces the need for huge labeled datasets. Deployed in voice assistants, captions, and meeting tools.
How it works
Audio (waveform or mel spectrogram) is converted to features (e.g. filter banks, learned representations). An acoustic model (e.g. conformer, wav2vec 2.0 encoder) maps features to frame- or segment-level representations. A decoder (CTC, RNN-T, or attention-based) produces text (characters or subwords). Modern systems are often end-to-end (waveform or features → text in one model). Self-supervised pretraining on unlabeled audio (e.g. wav2vec) then fine-tuning on labeled ASR data improves robustness and reduces labeled data needs.
Use cases
Speech technologies apply when the input or output is audio: transcription, assistants, and speaker or synthesis systems.
- Automatic speech recognition (ASR) for transcription and captions
- Voice assistants and spoken dialogue systems
- Speaker identification and speech synthesis (TTS)