Adobe Speech To Text V2.1.6 Para Premiere Pro 2... Access

Whisper achieves 2–3% lower WER in noisy conditions but requires external processing and lacks Premiere integration. Adobe’s advantage is seamless NLE workflow , not raw ASR supremacy. 4.2 Punctuation and Capitalization v2.1.6 adds automatic punctuation (periods, commas, question marks) and capitalization of sentence starts and proper nouns (limited accuracy: ~85% for common names, lower for rare entities). 4.3 Handling of Fillers The system can be configured to exclude “um,” “uh,” “like,” etc., from generated captions – a critical feature for clean subtitle output. 5. Performance Benchmarking Test system: Windows 11, Intel i9-13900K, NVIDIA RTX 4080, 64 GB RAM. Source: 1-hour 4K interview timeline, single speaker, 44.1 kHz audio.

| Audio Condition | WER (English US) | WER (Japanese) | WER (Spanish) | |----------------|----------------|----------------|----------------| | Studio microphone, no background noise | 4.2% | 7.1% | 5.8% | | On-location interview, mild traffic | 11.5% | 14.3% | 12.9% | | Group discussion, overlapping speech | 24% | 29% | 27% | | Strong accents (e.g., Scottish English) | 18% | N/A | N/A | Adobe Speech to Text v2.1.6 para Premiere Pro 2...

| Task | v2.0 (older) | v2.1.6 | Improvement | |------|-------------|--------|--------------| | Transcription time (GPU enabled) | 12 min | 7 min | | | Transcription time (CPU only) | 32 min | 28 min | 12% faster | | Multi-speaker labeling (2 speakers) | 2 min | 1.2 min | 40% faster | | Export to .SRT | 10 sec | 6 sec | 40% faster | Whisper achieves 2–3% lower WER in noisy conditions