Speech Recognition

Recognition models map acoustic evidence to word or token sequences. Connectionist Temporal Classification models align variable-length audio and text through paths that collapse repeated labels and blanks; encoder-decoder systems generate text conditioned on learned audio features. Decoding can use language constraints or prompts, which help resolve ambiguity but can also favor plausible words over weak acoustic evidence. Segmentation and contextual carryover matter for long recordings. Transcription is distinct from speaker diarization, which estimates who spoke when, and from speech translation, which changes language. Punctuation, capitalization and formatting are additional conventions whose correctness may differ from whether the spoken words were recognized.

Define whether the output must be verbatim, normalized or suitable for reading. Inspect sample rate, channels, overlap and recording quality, then select a recognizer with relevant language coverage. Keep speakers and recording sessions separate across evaluation splits. Use consistent text normalization when calculating word or character error rates, and separately check critical fields such as identifiers and amounts. Include silence, background speech and accented or mixed-language examples. The deliverable is a transcription pipeline with known quality by condition, documented decoding settings and a review or uncertainty path for errors that matter to the application.

Noise, overlapping speakers, unfamiliar names and weak audio can cause substitutions, omissions or invented speech. A language prior may hide poor acoustic evidence behind grammatical output. Aggregate error rate does not reflect the consequence of one wrong number. Diarization and timestamp quality need separate measurement. Recognition does not establish that a spoken statement is true. Compare transcripts with the recording and evaluate realistic speaker and device conditions, including inputs where the correct response is no transcription.