Research / ASR Lab

ASR Lab

Applied speech-recognition research behind Audio2SRT.app: engine selection, word timestamps, speaker segmentation, subtitle quality, and production reliability.

The goal is not to expose an internal vendor menu. The goal is to make ASR decisions measurable, then give users a single best-fit path for their media, language, timestamp, speaker, and export needs.

Research tracks

Engine selection

Compare production output by language, media type, timestamp needs, diarization quality, and failure recovery rather than by benchmark tables alone.

Word timestamps

Measure whether word-level timing is stable enough for editing, subtitle QA, search, and downstream agent workflows.

Speaker segmentation

Evaluate diarization as a separate deliverable: speaker boundaries, speaker consistency, mixed audio, and interview-style recordings.

SRT quality

Treat subtitle output as a product surface: sentence timing, readability, line breaks, punctuation, and review ergonomics.

Multilingual routing

Route jobs by observed language and output requirements without asking users to compare transcription engines or exposing internal vendor choices.

Product judgment

Why not expose a model selector?

Most users do not want to become ASR buyers. The product should choose the best-fit path from language, media kind, timestamps, speaker needs, and output format.

Why publish research on a tool site?

The tool page should stay clean, but a research hub helps serious users understand evaluation standards and gives search engines a structured explanation of the product's expertise.

Does Audio2SRT.app disclose its production routing?

No. The research explains evaluation criteria and observed market tradeoffs. Runtime routing remains an implementation detail.

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