Voice activity detection in contact centers: Why accurate speech detection defines CX success

Your contact center processes thousands of calls daily. Customers speak, pause, sigh in frustration, and wait on hold while background noise fills the silence between words. Traditional systems struggle to distinguish actual speech from the ambient chaos. Hold music bleeds into transcriptions, background conversations trigger false starts, and delayed speech recognition leaves customers repeating themselves.

Voice activity detection, or VAD, solves this problem at the source. As the foundational layer of modern voice AI systems, VAD determines precisely when human speech begins and ends. This technology enables everything from natural conversation flow to accurate transcription and intelligent routing. When enterprise contact centers handle 500,000+ calls monthly, the difference between accurate and inaccurate speech detection translates directly into customer satisfaction scores, operational costs, and human agent productivity.

Enterprise contact centers using AI voice agents, which rely on VAD for accurate speech segmentation, are already seeing results. The insurance provider BarmeniaGothaer reduced switchboard workload by 90% with their AI agent, while Berlin-Brandenburg Airport achieved a 65% cost decrease. McKinsey also found that contact centers implementing speech analytics achieve a 10% improvement in customer satisfaction (CSAT) scores.

For enterprises seeking to close the relationship gap between their brand and customers, accurate speech detection forms the foundation of meaningful voice interactions.

Voice activity detection explained

Voice activity detection is a signal-processing technique that distinguishes speech from silence or background noise in audio streams. VAD acts as the first filter in a voice AI system: it determines when someone is actually speaking so that only relevant audio reaches the speech recognition engine.

Consider a customer calling to report an insurance claim. They speak, pause to check their policy number, shuffle papers, then continue. But in the background, their television plays, and a dog barks.

A VAD system continuously analyzes this audio stream to identify moments of actual speech, filter out environmental noise, and recognize pauses as silence rather than conversation end. When the customer resumes speaking, VAD signals the speech recognition system to process only those specific moments.

Types of voice activity detection

Not all VAD technology works the same way. The right approach for your contact center depends on computational resources, noise conditions, and accuracy requirements.

Classical signal-based methods analyze audio characteristics like amplitude variations and frequency patterns. These approaches offer low computational complexity but often struggle with noisy contact center environments.

Machine learning and deep learning methods use neural networks that learn optimal detection patterns from large datasets. These models, including architectures based on recurrent neural networks, have achieved high accuracy across varied noise environments without manual tuning.

Hybrid approaches combine classical efficiency with neural network accuracy. Research from Sony demonstrates that simpler feature combinations can deliver robust performance suitable for enterprise deployments without complex architectures.

Modern neural network-based systems like the open-source technology Silero VAD process audio in less than one millisecond per chunk on a single CPU thread.

How does voice activity detection work?

VAD operates through four distinct processing phases continuously and in real time:

1. Audio frame analysis breaks continuous audio into small chunks

2. Speech/silence classification distinguishes actual speech from background noise

3. Non-speech filtering removes irrelevant frames

4. Forward triggering signals the next step in the system to start processing

Consider how this works in practice: A traveler calls an airline to rebook a canceled flight. They say, "I need to change my flight to tomorrow morning," while airport announcements echo in the background. VAD immediately identifies the speech segment and filters out the ambient terminal noise. This triggers speech recognition, which converts the words to text.

The AI agent then processes the intent, searches available flights, and offers rebooking options — all within moments. Without accurate VAD at the foundation, the system might mistake background announcements for speech, interrupt the customer mid-sentence, or miss their request entirely.

Benefits and challenges of voice activity detection

CX leaders building a business case for VAD-powered AI voice agents need both sides of the story: the measurable returns that justify investment and the implementation realities that shape success.

Here's what accurate speech detection enables for enterprise contact centers:

• Natural conversation flow: VAD enables proper turn-taking between customers and AI agents, ensuring the system listens and responds only when a customer is actually speaking. When combined with emotion detection, VAD can also help identify frustrated callers early, reducing call abandonment.

• IVR modernization: VAD triggers natural language understanding so customers can state their needs conversationally rather than pressing through menu options. This transforms the voice channel from a rigid menu system into a responsive experience.

• Processing cost reduction: Every non-speech frame that VAD filters out represents compute power that speech recognition services never consume, reducing the per-interaction cost of voice AI at scale. For contact centers handling hundreds of thousands of calls monthly, even small efficiency gains per call compound into meaningful infrastructure savings.

• Faster compliance workflows: By accurately segmenting speech from silence, VAD ensures that only actual conversation content reaches the compliance monitoring systems that flag sensitive information (like credit card numbers or personal health data). This reduces the volume of audio that needs to be reviewed and speeds up the auditing process.

However, implementation comes with a few challenges:

• Technology without integration fails: Despite a 15% increase in AI adoption in contact centers from 2023 to 2025, Deloitte found that organizations experienced an average 0.5-point drop in customer and employee experience ratings. The reason: contact centers struggle to integrate new technology with existing platforms. For VAD specifically, this means speech detection accuracy depends on how well it connects to speech recognition, natural language understanding, and your existing telephony infrastructure.

• Integration takes planning: Deploying VAD within an existing contact center requires careful orchestration between speech detection, recognition, and routing systems. Enterprise buyers should expect dedicated integration effort, especially in multi-vendor environments.

• Accuracy varies by environment: VAD systems tuned for one acoustic profile can underperform when customers call from unpredictable settings: a quiet home office, a crowded airport terminal, or a car on the highway. Contact centers handle all three in the same hour. Getting consistent speech detection accuracy across these conditions requires ongoing calibration, as default settings rarely work well out of the box.

Choosing the right VAD-powered platform matters. Here are leading solutions for contact center teams looking to improve speech detection.

Popular tools that use voice activity detection

Choosing the right VAD-powered platform matters. Let’s go over some of the leading solutions for contact center teams looking to improve speech detection.

Several enterprise platforms embed VAD directly into their voice AI stack, so contact center teams can deploy without building the speech detection layer themselves:

• Parloa: Provides an AI Agent Management Platform to the full lifecycle of AI agents across four phases (Design, Test, Scale, Optimize) with natural language briefings; ISO 27001, SOC 2, PCI DSS, HIPAA, and DORA certified.

• Cisco Webex: Integrates VAD into its enterprise voice AI pipeline with approximately 1.3-second latencies over real telephony paths.

• Deepgram: Provides VAD as part of their speech recognition API, reducing compute costs by processing only speech frames.

Other tools provide VAD as standalone components or developer infrastructure. These offer more flexibility but require engineering resources to integrate into an existing contact center environment:

• Silero VAD: Open-source neural network-based VAD that processes audio in under one millisecond per chunk. Widely adopted as the speech detection layer in platforms like NVIDIA Riva and LiveKit.

• NVIDIA Riva: Distributes Silero VAD through their NGC Catalog as part of a broader GPU-accelerated speech AI toolkit. Requires developer resources to deploy and integrate.

• LiveKit: Implements Silero VAD as a core plugin for turn detection in real-time WebRTC voice applications. Designed for developers building custom voice AI systems.

Where VAD lives in your stack matters as much as how well it detects speech. The most effective enterprise deployments pair accurate speech detection with the systems that act on it. That means speech recognition, intent routing, and agent orchestration working together from the first customer interaction through resolution.

Transform your enterprise contact center with voice activity detection

Voice activity detection has matured from a niche signal-processing technique into foundational infrastructure for enterprise CX. But accurate speech detection alone doesn't guarantee results. What matters is how you design, test, deploy, and improve the voice AI that sits on top of it.

CX leaders face mounting pressure to reduce costs while improving satisfaction. Accurate speech detection provides the technical foundation for meaningful voice interactions at scale. And VAD makes it possible to transform the voice channel from a cost center into a relationship-building engine.

We built Parloa's AI Agent Management Platform to help contact centers implement VAD-powered voice AI that delivers results — without getting stuck in pilot purgatory.

Here's what that means in practice:

• Design with natural language: Build AI agents using intent-driven briefings rather than scripted flows, reducing technical complexity while maintaining control.

• Test before deployment: Simulation-driven testing validates AI behavior across real-world scenarios before production, not after problems emerge.

• Scale globally: 130+ language support with speech capabilities fine-tuned for regional nuance enables enterprises to serve diverse customer populations.

• Ensure compliance: ISO 27001, SOC 2, PCI DSS, HIPAA, and DORA certifications provide the security rigor regulated industries demand.

Parloa is a platform for building and scaling voice AI that combines natural conversation quality with enterprise reliability. We help CX leaders move from pilot to production while maintaining the compliance and customer-focused service your brand demands.