Speech and voice technology: a guide for voice AI startups

Voice is humans' most natural way to communicate. Advances in speech recognition and synthesis are opening new products from call centres to health, education and in-car systems.

Core technologies

• Speech recognition (speech-to-text): transcribing speech
• Text-to-speech: natural reading and speech generation
• Speaker diarisation: who spoke when in a recording
• Voice assistants: real-time combination of recognition, a language model and synthesis; see how LLMs work

Use cases

• Call centres: transcripts, summaries, quality analysis, automated responses; see AI in customer service
• Meetings: transcription and summaries
• Health: automatic clinical notes; see health tech
• Education: language learning and pronunciation feedback; see edtech
• Accessibility: captions, read-aloud
• Content: voice-over and podcast production

Challenges in Turkish speech

• Regional accents and dialects
• Mixed Turkish and English terms
• Low-quality, noisy phone lines
• Names, brands and industry terms

Testing on your own field recordings and adding domain vocabulary improves quality markedly; see Turkish NLP.

Privacy and regulation

• Informing participants and, where needed, getting consent for recording
• Voice recordings are personal data; biometric uses such as voiceprints may count as special category data.
• Retention periods and access control

See AI and KVKK.

Misuse risk

Realistic voice cloning raises fraud and impersonation risk. Products offering cloning should build in consent verification, usage limits and abuse detection; see responsible AI.

Business models

• Per-minute or per-hour usage fees
• Software subscriptions for call centres and enterprises
• End-to-end solutions for a specific sector; see vertical SaaS

Conclusion

Voice technology offers real opportunities for local startups, especially in languages like Turkish. High accuracy in field conditions and strong privacy safeguards are the keys.