This article will detail how to build an AI voice dialogue system that can rival human conversation.
You can try out the real-time voice AI dialogue demo page if interested.
We've been saying "Hey, Siri" for many years now. However, as AI technology continues to advance, Siri seems increasingly outdated.
At Apple's 2024 launch event, the company announced that it would introduce the latest AI technology to Siri, enhancing its intelligence.
Traditional AI Dialogue Transmission Scheme
The fluency of our interactions with AI is mainly influenced by two key factors: the efficiency of understanding and generating responses by large models, and the speed of network transmission.
Firstly, the efficiency of understanding and generating by large models determines the accuracy and timeliness of AI responses. Modern AI voice dialogue systems rely on deep learning and natural language processing technologies, using vast pre-trained models to understand user intent and generate appropriate replies.
Secondly, the speed of network transmission is also a crucial factor affecting interaction fluency. AI voice dialogue systems typically run in the cloud, requiring the user's voice input to be transmitted to the server for processing, and the generated response to be sent back to the user's device.
Achieving breakthrough speed improvements in models is very challenging, requiring either more substantial computing resources or further model optimization. Therefore, much of the optimization work focuses on data transmission.
Traditional AI voice dialogue systems usually use the WebSocket scheme for real-time communication. WebSocket is a communication protocol based on TCP that allows full-duplex communication between client and server, meaning both can send and receive data simultaneously.
// server.js
const WebSocket = require('ws');
const server = new WebSocket.Server({ port: 8080 });
server.on('connection', (ws) => {
console.log('A new client connected!');
ws.on('message', (message) => {
console.log(`Received: ${message}`);
ws.send(`Server received: ${message}`);
});
ws.on('close', () => {
console.log('Client disconnected');
});
});
console.log('WebSocket server is running on ws://localhost:8080');
However, its TCP-based nature brings some challenges, especially regarding low latency and high real-time requirements in voice interactions. TCP, being a connection-oriented, reliable transmission protocol, involves a three-way handshake to establish a connection. While these features enhance transmission reliability, they also increase communication overhead and latency. In practical applications, this back-and-forth data transmission model can result in 2-3 seconds of delay.
Moreover, WebSocket faces challenges in handling high concurrent connections. As the number of users increases, the server needs to maintain a large number of long connections, putting higher demands on the server's resources (such as memory and processing power).
New AI Dialogue Scheme Based on WebRTC
The new generation of AI voice technology, like GPT-4o, adopts a real-time voice scheme based on WebRTC, significantly enhancing voice interaction fluency and user experience. Compared to the traditional WebSocket scheme, WebRTC has noticeable advantages in terms of latency and weak network resistance.
What is WebRTC?
WebRTC (Web Real-Time Communication) is an open-source project that supports real-time communication in browsers and mobile applications. It achieves low-latency data transmission through peer-to-peer (P2P) connections.
Unlike WebSocket, WebRTC uses the UDP protocol, reducing communication latency by minimizing handshake and acknowledgment processes. Ideally, latency can be as low as milliseconds, providing a more real-time voice interaction experience.
This core technological breakthrough enables GPT-4o to respond to audio inputs within hundreds of milliseconds, achieving human-like conversation speeds.
Noise Cancellation in Dialogue
WebRTC not only supports data channels but also has robust audio and video processing capabilities. It features built-in echo cancellation, noise suppression, and automatic gain control, ensuring clear voice communication in various environments. This is crucial for AI voice dialogue systems, as clear voice input is fundamental for accurately understanding user intent.
1. Echo Cancellation: Echo Cancellation is a key feature of WebRTC that aims to eliminate echo. When a device's microphone captures sounds played from its speaker, echo can occur. WebRTC uses algorithms to detect and eliminate these echoes, ensuring the clarity of voice communication.
2. Noise Suppression: Noise Suppression filters out background noise, such as wind, traffic, and other environmental noises. WebRTC’s advanced noise suppression algorithms significantly reduce background noise interference while maintaining voice clarity.
3. Automatic Gain Control: Automatic Gain Control (AGC) adjusts the microphone's input volume to ensure stable and consistent voice signals. Whether the user is speaking softly or loudly, AGC dynamically adjusts the volume, keeping the voice input within an appropriate range.
Mature and Quick Access Solutions
Compared to using native WebRTC, developers can seamlessly integrate AI dialogue functions into various applications quickly through TRTC (Tencent Real-Time Communication), without delving into complex technical details, significantly shortening the product development cycle.
The TRTC Conversational AI Solution provides end-to-end capabilities from audio and video capture, processing, and transmission to cloud AI processing services. The client captures audio through the TRTC SDK and sends it to the cloud, where it is processed by AI services. TRTC provides comprehensive SDKs and API documentation, along with rich, out-of-the-box scenario-based components for developers.
For noise reduction, the TRTC one-stop solution uses Tencent AI Lab's proprietary noise reduction algorithms, leveraging deep learning to intelligently detect and remove noise interference in the transmission signal, thus improving voice quality and intelligibility. Accurate STT recognition paired with intelligent noise reduction ensures accurate capture and conversion of user voice even in noisy environments.
Moreover, businesses can customize the input and output in the AI dialogue process according to their needs. For example, for intelligent interruption functionality, the TRTC Conversational AI Solution offers three modes: automatic interruption, custom interruption, and no interruption. Businesses can flexibly customize interruption logic to suit their scenarios through custom interruption.
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