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Transcription Speech-to-Text

⬢ LIVELLO 2Tecniche
Medio
Impatto sullo stipendio
3 mesi
Tempo di apprendimento
Medio
Difficoltà
1
Carriere
In sintesi

Speech-to-text (ASR) converts audio into text automatically. Used by accessibility teams, content creators, and developers building voice interfaces. Salary: $70-120k junior, $120-180k mid, $180-270k senior. Learn in 3-4 weeks. Adjacent to NLP, audio processing, and machine learning.

Cos'è Transcription Speech-to-Text

Speech-to-text (ASR, automatic speech recognition) converts audio recordings into text automatically. Modern ASR models (Whisper, Google Cloud, AWS Transcribe) achieve >95% accuracy on clean audio and can handle multiple languages, accents, and dialects. Applications range from accessibility (captions for deaf users), content creation (podcast transcripts, video subtitles), to voice interfaces (Alexa, Siri). ASR combines audio signal processing, acoustic modeling, and language models to predict what words were spoken.

🔧 STRUMENTI ED ECOSISTEMA
Whisper (OpenAI)Google Cloud Speech-to-TextAWS TranscribeAssemblyAIPythonPyAudioFFMPEGHugging Face transformers

💰 Stipendio per regione

RegioneLivello baseMidLivello esperto
USA$70k$145k$240k
UK£50k£100k£160k
EU€55k€110k€175k
CANADAC$65kC$130kC$220k

🎯 Carriere che usano Transcription Speech-to-Text

Voice Ai Engineer

❓ Domande frequenti

What's the difference between Whisper, Google Speech-to-Text, and AWS Transcribe?
Whisper is open-source and free but slower. Google and AWS are cloud APIs, faster and more accurate for clean audio. Whisper is better for multiple languages and robustness to accents.
How accurate are modern ASR models?
On clean audio, >95% accuracy. On noisy audio (car, crowd), 70-85%. Accuracy varies by language and dialect. Fine-tuning on your data improves accuracy by 5-10%.
Can I use ASR for real-time transcription?
Yes, but with latency tradeoffs. Real-time ASR (streaming) has 1-5 second delay. Batch transcription (all at once) is more accurate but has longer total latency.
What about accents and non-native speakers?
Modern ASR handles accents reasonably well, but accuracy drops 10-20% for non-native speakers. Fine-tuning on accent data helps. Background noise hurts accuracy more than accents.
Can I handle multiple speakers?
Yes, via speaker diarization (identifying which speaker is speaking). Whisper doesn't do this natively; use Pyannote or external services. Google Cloud has speaker diarization built-in.

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