@TrelisResearch on Backlist

2 appearances on the backlist front page in the last 30 days.

54.

Multi-speaker Transcription: Who said What and When?

Multi-speaker Transcription: Who said What and When? On 10 real multi-speaker CHiME / NOTSOFAR meetings, Trelis edges AssemblyAI on corpus cpWER. - Same single-channel audio. - Same meeteval scoring. - No oracle speaker labels. Trelis tra

by @TrelisResearch (Trelis Research) · backlist 2026-06-03 · rubric 77.0

80.

Audio Dataset Cleaning: All that glisters is not Gold

Audio Dataset Cleaning: All that glisters is not Gold -- Said "high quality" audio datasets are not always high quality, which can leave you puzzled when training doesn't make your transcription or TTS model any better. The most robust app

by @TrelisResearch (Trelis Research) · backlist 2026-06-02 · rubric 66.0