Implement text analysis solutions 4.2 Implement speech solutions
A contact center needs a voice agent with low-latency speech-to-speech conversations…
Study guide skill: Implement workflows to convert speech to text and text to speech for agentic interactions
Question B2-024
A contact center needs a voice agent with low-latency speech-to-speech conversations, noise suppression, echo cancellation, and natural end-of-turn detection. The team doesn't want to orchestrate separate speech-to-text, LLM, and text-to-speech components or deploy models itself. What should you use?
Select one answer.
Answer: D
The Voice Live API is a fully managed speech-to-speech solution that combines speech recognition, generative AI, and text to speech in one interface, so you don't orchestrate components or deploy generative models. It adds conversational features such as noise suppression, echo cancellation, interruption detection, and advanced end-of-turn detection. Chaining batch services adds latency and engineering complexity, and the other options don't handle voice conversations.
Deep dive
How it works
The Voice Live API is built for low-latency, high-quality speech-to-speech interactions for voice agents. It brings speech recognition, generative AI and text to speech together in one unified interface, so you don't orchestrate several components yourself. It's fully managed: you send audio in and get audio out (plus avatar visuals and action triggers), and you don't deploy or manage any generative AI models.
Its conversational features match the question's list:
- Noise suppression: reduces environmental noise.
- Echo cancellation: stops the agent from picking up its own responses.
- Robust interruption detection: recognizes when the user interrupts.
- Advanced end-of-turn detection: allows natural pauses without ending the turn too early.
You can choose from several generative models, add phrase lists or custom speech models for input, and use custom voice for output. Contact centers are one of the key scenarios the docs list, for customer support and self-service voice bots.
The other options, and when you'd use them
- Batch transcription, then Chat Completions and batch synthesis: The docs note that chaining separate modules adds engineering complexity and latency the user can notice. Batch transcription also returns results asynchronously, so it isn't built for live conversations.
- Azure Language conversational PII detection: Conversation PII handles multi-turn chat and transcript payloads to detect and mask personal data. It doesn't hold a voice conversation.
- Custom Translator: This builds custom translation systems from parallel documents. It's a text translation feature.
Exam tip
"Speech-to-speech voice agent, no orchestration, no model deployment" plus noise suppression or end-of-turn detection means Voice Live.
References
Last reviewed: