Speech and voice agents

Speech and voice agents interact with users through spoken dialogue. These agents integrate speech recognition, natural-language understanding, and speech synthesis to enable conversational AI across telephony, mobile, web, and embedded platforms.

Voice agents are particularly effective in hands-free, real-time, or accessibility-driven environments. By combining streaming interfaces with LLM-powered reasoning, they facilitate rich, dynamic interactions that feel natural to users.

Architecture

A speech and voice agent is shown in the following diagram:

Description

1. Receives a voice query

   • The user voices a request to a phone, microphone, or embedded system.

   • A speech-to-text (STT) module converts the audio to text.

2. Integrates streaming and telephony context

   • The agent uses a streaming interface to manage audio I/O in real time.

   • If it's deployed in a contact center or telecom context, telephony integration handles session routing, dual-tone multi-frequency (DTMF) input, and media transport.

Note: DTMF refers to the tones generated when you press buttons on a telephone keypad. In the context of streaming and telephony context integration within voice agents, DTMF is used as a signal input mechanism during a phone call, especially in interactive voice response (IVR) systems. DTMF inputs enable the agent to:

• Recognize menu selections (for example, "Press 1 for billing. Press 2 for support.")

• Collect numeric inputs (for example, account numbers, PINs, and confirmation numbers)

• Trigger workflows or state transitions in call flows

• Revert from speech to touch-tone when necessary

1. Reasons through LLM stream context

   • The query is sent to the agent, which passes it, along with any session metadata (for example, caller ID, prior context), to an LLM.

   • The LLM generates a response, possibly using a chain-of-thought strategy or multiturn memory if the interaction is ongoing.

2. Returns a voice response

   • The agent converts its response to speech using text-to-speech (TTS).

   • It returns audio to the user through a voice channel.

Capabilities

• Real-time speech understanding and generation

• Multilingual I/O with STT and TTS support

• Integration with telephony or streaming APIs

• Session awareness and memory handoff between turns

Common use cases

• Conversational IVR systems

• Virtual receptionists and appointment schedulers

• Voice-driven helpdesk agents

• Wearable voice assistants

• Voice interfaces for smart homes and accessibility tools

Implementation guidance

You can build this pattern using the following tools and AWS services:

• Amazon Lex V2 or Amazon Transcribe for STT

• Amazon Polly for TTS

• Amazon Chime SDK, Amazon Connect, or Amazon Interactive Video Service (Amazon IVS) for streaming and telephony

• Amazon Bedrock for reasoning with Anthropic, AI21, or other foundation models

• AWS Lambda to connect STT, LLM, TTS, and session context

(Optional) Additional enhancements may include the following:

• Amazon Kendra or OpenSearch for context-aware RAG

• Amazon DynamoDB for session memory

• Amazon CloudWatch Logs and AWS X-Ray for traceability

Summary

Speech and voice agents are intelligent systems that interact through natural conversations. By integrating speech interfaces with LLM reasoning and real-time streaming infrastructure, voice agents enable seamless, accessible, and scalable interactions.