AI voice assistants are being integrated deeply and used broadly in daily life. They have been brought to the entire family of apps including WhatsApp, Instagram, Facebook, Messenger, and the standalone MAI app. Their typical use cases include social coordination, information gathering, entertainment, and productivity.
One of the main challenges with using AI voice assistants in real-time communication is interferences, which can be very disruptive to the user experience.
There are three main types of audio inputs that can trigger the AI unintentionally: background noise, side conversation or side speech, and acoustic echo. Unlike humans who can recognize and tolerate background noise, side speech, and echo through cognitive capability trained from infancy, the AI bot does not have such power. It is necessary to bring such capability to the AI.
More than 50% of conversations with AI assistants experience some sort of background noise or side speech, such as television being turned on in the background or people talking nearby in a crowded place. These interferences could falsely trigger the AI bot to respond or interrupt when the bot is responding. The AI bot could also respond to its own echo if the user’s device does not have a good acoustic echo canceller or AEC.
The solution to the interference problem is a multi-layered audio AI stack. Its goal is to make conversation with AI as natural, engaging, and responsive as talking to a human.
Traditional Real-Time Communication Audio Stack
A typical audio stack for human real-time communication works as follows.
- On the sender side, the audio signal is captured by the microphone and goes through on-device audio processing including acoustic echo canceller (AEC), noise suppression (NS), and automatic gain control (AGC).
- The processed audio signal is encoded, packetized, and sent to the network at fixed intervals, for example every 20 milliseconds.
- On the receiver side, the audio packet is received in a jitter buffer to absorb jitter or packet loss, decoded, processed, and played through the loudspeaker.
This architecture works effectively for human conversations but is unfit for human-machine real-time communication because of differences in communication requirements.
Why Human–Machine Communication Has Different Requirements
In human-machine communication, this architecture changes due to new requirements. The user expects immediate connection with the bot after initiating a call. Response latency and packet loss resilience are more important because humans expect the bot to be responsive and understand requests. Audio processing should preserve more of the user’s voice while cleaning up noise, echo, or other sounds..
In real-time communication systems, speech is captured and sent over the network in small pieces called audio packets. Each packet represents a short fragment of sound and is transmitted continuously while the user is speaking. As these packets travel over the network, they may arrive at the receiving system at uneven time intervals. Some packets arrive early, some arrive late, and some may be lost. This variation in packet arrival time is known as jitter.
Recording and buffering audio start as soon as the microphone is open. After connection to the server is made, buffered audio packets are sent faster than real time. On the server side, the jitter buffer and decoder absorb and decode the burst of packets. This is possible because the bot does not require audio packets at fixed intervals like humans do.
To manage jitter, the receiving system uses a jitter buffer. The jitter buffer temporarily stores incoming audio packets and releases them in a controlled sequence. This allows the system to smooth out timing variations and maintain a consistent audio stream.
In human–machine communication, buffering delays are kept as small as possible to support fast responses. The jitter buffer is optimized for loss recovery rather than network jitter, keeping delay as small as possible while maintaining packet loss resilience, even if small timing variations remain.
AEC and noise suppression are tuned to be more robust in double talk, preserving more of the user’s voice even with some echo and noise leakage. This background processing ensures that audio is delivered in a stable and usable form for real-time speech processing while meeting strict latency requirements.
Why can’t we just fix Interference inside the LLM for Real-Time Communication
Large language models are not interference robust by default. To enable real-time communication, the system must be robust to real-life distractors such as echo, noise, and side speech. One approach to make them potent is data-driven training with background noise, background speech, and echo leakage. This gives the model basic robustness but reaches limits due to conflicting training requirements. Once the LLM decides to ignore user prompts, it becomes difficult to debug and tune it.
This motivates the design of a modular conversation fluidity stack. Modularity provides debugability, flexibility, explainability, and rapid iteration based on user feedback. The system prioritizes control, transparency, and rapid iteration rather than only performance. A multi-layered audio AI stack supplements the LLM against interferences more robustly.
Multi-Layer Audio AI Stack Architecture
In the multi-layer audio AI stack, interferences are addressed layer by layer before passing input to the language model. The components of the Multi-Layer Audio AI Stack Architecture are:
- Noise suppressor
- Voice clarity detector
- Primary speaker segmentation
- Echo detection and mitigation
Background noise is reduced using a noise suppressor. Foreground speech versus background speech is detected using a voice clarity detector. The main user speech is identified with primary speaker segmentation. Echo of the bot’s audio is detected and suppressed in the final layer.
Noise Suppressor
Background noise is reduced using a noise suppressor. Noise suppression eliminates non-speech background noise and works with a voice clarity detector that determines whether captured speech is device-targeted speech or background speech.
Voice Clarity Detector
Foreground speech versus background speech is detected using a voice clarity detector. The voice clarity detector uses a voice activity detector to detect speech activity and identify whether speech is foreground or background based on clarity, centroid, and energy.
Primary Speaker Segmentation
The main user speech is identified with primary speaker segmentation. The primary speaker segmentation module detects whether device-targeted speech is from the main user or a secondary person or sound source. The architecture combines classical digital signal processing and deep neural networks. Speech features such as pitch and MFCC are extracted and fed to a neural network that generates speaker signatures in an enrollment-free manner.
Echo Detection and Mitigation
Echo of the bot’s audio is detected and suppressed in the final layer. Echo is particularly disruptive to conversations with AI because the AI bot has little tolerance for echo. If a device has echo leaks, the AI bot may respond to echo as if it were actual user input.
Echo mitigation is implemented on both client and server sides. On the client side, software AEC is enabled. On the server side, an ML-based mitigation uses primary speaker segmentation to model bot audio echo signals, and a DSP-based echo suppressor computes an adaptive gain factor based on residual echo level. A full-duplex echo control system is critical for smooth AI conversations.
FAQs about Real-Time Communication Challenges in AI Voice Assistants
1. What are the main real-time communication challenges faced by AI voice assistants?
One of the main challenges with using AI voice assistants in real-time communication is interferences, which can be very disruptive to the user experience. There are three main types of audio inputs that can trigger the AI unintentionally: background noise, side conversation or side speech, and acoustic echo.
2. Why does the traditional real-time communication audio stack not work well for AI voice assistants?
Traditional real-time communication stacks are designed for human-to-human conversations because humans can recognize and tolerate background noise, side speech, and echo through cognitive capability trained from infancy, the AI bot does not have such power. A traditional stack is unfit for human-machine real-time communication because of differences in communication requirements. The user expects immediate connection with the bot after initiating a call. Response latency and packet loss resilience are more important because humans expect the bot to be responsive and understand requests. Audio processing should preserve more of the user’s voice while cleaning up noise, echo, or other sounds
3. What is a multi-layer audio AI stack and why is it needed?
In the multi-layer audio AI stack, interferences are addressed layer by layer before passing input to the language model. The components of the Multi-Layer Audio AI Stack Architecture are noise suppressor, voice clarity detector, primary speaker segmentation, and echo detection and mitigation. Background noise is reduced using a noise suppressor. Foreground speech versus background speech is detected using a voice clarity detector. The main user speech is identified with primary speaker segmentation. Echo of the bot’s audio is detected and suppressed in the final layer.
4. Why can’t interference be fully fixed inside the LLM for real-time communication?
Large language models are not interference robust by default. To enable real-time communication, the system must be robust to real-life distractors such as echo, noise, and side speech. One approach to make them potent is data-driven training with background noise, background speech, and echo leakage. This gives the model basic robustness but reaches limits due to conflicting training requirements. Once the LLM decides to ignore user prompts, it becomes difficult to debug and tune it.
5. What are the key components of the multi-layer audio AI stack?
The components of the Multi-Layer Audio AI Stack Architecture are:
- Noise suppressor
- Voice clarity detector
- Primary speaker segmentation
- Echo detection and mitigation
