stt-interpreter

To analyze speech-to-text logs for readability, you review STT experiment inputs from multiple engines to extract actionable punctuation heuristics. This process surfaces structured rules that refine automated transcription workflows and produce cleaner text outputs.

Effective punctuation heuristics for English STT transcriptions are derived by logging comparative experiments across input methods like Termux Gemini CLI, Android GUI, and WhisperIME. Analyzing these logs identifies consistent patterns to apply for automated punctuation insertion.

You can compare STT engine outputs across different input methods by logging experiments from Termux CLI, Android GUI, and WhisperIME. The analysis targets English-39 and English-74 monolingual transcriptions to extract specific readability and punctuation rules.

You extract actionable notes from STT experiment logs by analyzing the transcription data to identify effective heuristics. These notes are then structured as memory artifacts suitable for storage in Gemini-style memory files for future workflow refinement.

You need YAML frontmatter containing name and description fields to process STT transcription logs. This metadata is required to properly structure the analysis and generate the resulting heuristics and memory artifacts for your transcription workflows.

The limitation of using heuristics for STT punctuation analysis is that the scope is restricted to monolingual English-39 and English-74 transcriptions. Heuristics extracted are specific to the tested engines and input methods, requiring continuous logging to adapt to new contexts.