How to Use Speech Recognition in Call Centers

Call center teams handle constant conversations while balancing strict compliance rules and customer expectations. Agents spend valuable time on manual notes, and managers struggle to track insights across thousands of calls. The strain shows up in long wrap-up times, inconsistent records, and missed opportunities to improve service.

By capturing spoken language in each conversation, transcripts can speed post-call summaries, surface compliance risks, and reveal customer trends. But transcription alone isn’t enough. The real value comes when speech-to-text connects with artificial intelligence (AI) voice assistants that understand intent and manage dialogue in real time.

Here, we’ll explore the benefits of speech recognition, how it fits into an AI assistant workflow, what to consider when choosing a provider, and how to integrate it with The Rasa Platform.

Key benefits of using speech-to-text tools

Speech-to-text tools can unlock a range of benefits to make calls faster, more efficient, and more customer friendly. In fact, speech analytics can boost customer satisfaction by 10% or more, increase sales, and cut costs by 20–30%.

Here are four key ways speech-to-text can add value to your contact center.

Automate post-call summaries

Automating post-call documentation is one of the most common and effective uses of speech recognition. Instead of agents spending unnecessary time tediously typing notes, conversations can be transcribed in real time or after the call. Those transcripts feed directly into ticketing platforms, customer relationship management (CRM) system entries, or knowledge bases, generating summaries automatically.

The impact of this is twofold: agents gain more time to focus on customers, and organizations get consistent records of compliance and performance tracking. In high-volume call centers, this simple step can save hundreds of hours each week, driving greater operational efficiency and strengthening service quality.

For example, one leading energy provider cut up to 60 seconds off customer authentication by combining speech recognition with AI, reducing handle time and enhancing the customer experience.

Analyze conversations for insight

Transcribed calls are a goldmine for understanding customer interactions at scale. Once a call is converted to text, it can be analyzed (alongside thousands of others) to reveal speech patterns, apply sentiment analysis, and identify recurring pain points.

For example, transcripts can highlight frequent customer questions, show where agents may need additional training, or surface compliance risks that might otherwise be missed. Over time, analytics can track sentiment patterns, flagging frustrated customers early and giving your team the chance to step in before issues escalate.

With speech recognition feeding insights directly into dashboards or AI-powered analytics tools, call centers gain a clearer view of performance and overall customer satisfaction.

Support multilingual and accessibility goals

Speech-to-text technology enhances a call center’s ability to serve diverse audiences. Once calls are transcribed, transcripts can be translated into multiple languages, helping agents and AI assistants support customers around the globe without relying solely on bilingual staff.

The same transcripts can also drive real-time captions or subtitles, making conversations more accessible for people with hearing impairments and supporting compliance with standards like the ADA.

Together, these capabilities expand inclusivity, strengthen trust, and allow businesses to deliver more equitable and consistent service across regions and demographics.

Fuel smarter voicebots and routing

Speech transcriptions can power more intelligent voicebots and automated call routing. By turning spoken words into structured text, AI voice assistants can quickly recognize customer intent, decide the next best step, and either guide callers through self-service options or route them to the right agent.

This approach reduces hold times, minimizes misrouted calls, and speeds up high-volume requests. For example, an AI voice chatbot might detect whether a customer wants to check a balance, report an issue, or update contact details, then respond with clear, context-aware guidance.

When paired with a robust natural language understanding (NLU) system like Rasa’s, these assistants become even more reliable in getting customers the answers they need.

Explore how Rasa’s NLU powers exceptional AI voice assistants.

How speech recognition works in an AI assistant workflow

Speech recognition plays a key role in the AI assistant workflow, from capturing a caller’s voice to transcribing it and passing it into systems that can understand intent and respond.

Audio input from the caller

The process begins the moment a customer speaks, whether through a phone-based interactive voice response (IVR) system, a web-based voice interface, or another automated channel. The system captures that audio and converts it into a digital signal that transcription software can process.

Accurate audio capture is critical. Background noise, call quality, and speaker clarity all influence how well speech-to-text tools perform. That audio becomes the foundation for every step that follows, from transcription to intent detection to the AI-driven responses that get customers the help they’re looking for.

Real-time or asynchronous transcription

Once audio is captured, it can be converted into text in two ways:

• Real-time transcription: Runs as the conversation happens. It powers voicebots and interactive assistants, enabling immediate responses and intent-based routing.
• Asynchronous transcription: Runs after the call ends. These transcripts support quality assurance, coaching, compliance monitoring, and analytics by surfacing patterns and sentiment across large call volumes.

Together, these approaches ensure transcription supports both real-time responses and deeper post-call analysis.

Routing the transcript to an AI model

Once speech is transcribed, the text can be fed into a conversational AI engine like Rasa. The AI analyzes the transcript to detect customer intent, extract key details, and decide the next step—whether that’s providing an answer, escalating to a human agent, or triggering workflows like ticket creation or service updates.

By connecting transcripts to a conversational AI model, call centers can interpret dialogue in context rather than as isolated statements. This leads to more accurate routing, personalized responses, and smoother handoffs between automation and live agents.

Feedback loop and improvement

Speech recognition and conversational AI improve through feedback. By reviewing transcripts where the assistant misinterpreted intent, missed context, or encountered transcription errors, teams can identify gaps in both the automatic speech recognition (ASR) and NLU systems.

This ongoing review refines models, updates training data, and strengthens dialogue management. For enterprises, maintaining a structured review process ensures they can turn raw speech data into actionable insights and deliver stronger customer support.

What to consider when choosing a speech recognition provider

The speech recognition market is expanding quickly, and many vendors promise better outcomes for both customers and agents. In a recent survey of 1,000 U.S. adults who had engaged with AI-based customer support, 70% said AI improved their self-service experience—a reminder that technology choices directly shape how customers perceive service quality.

Accuracy and language support

Accuracy should be a top priority when evaluating speech recognition tools. Misinterpreted words can frustrate callers, create errors in ticketing, or introduce compliance risks. Just as important is ensuring the system supports the languages and dialects your audience uses, especially for global call centers.

It’s also critical to decide whether you need real-time call transcription or post-call processing:

• Real-time transcription powers live voicebots, automated routing, and immediate agent support.
• Post-call transcription is better suited for analytics, quality checks, and spotting trends across large volumes of customer conversations.

Choosing the right approach that matches your use case ensures call transcripts are reliable, actionable, and valuable for both operations and customer experience.

Integration with your AI stack

Beyond accuracy, the right speech recognition provider should connect seamlessly with your AI stack. Look for tools that offer flexible Application Programming Interfaces (APIs) or open-source support, so they can plug into conversational AI systems, analytics platforms, or your CRM without heavy customization.

For teams using Rasa, this is especially important. Providers that feed transcripts directly into Rasa’s NLU pipeline let your assistant understand intent, manage dialogue, and respond in context. Done well, speech recognition drives faster, more consistent customer interactions.

Data privacy and compliance

When handling voice data, security and compliance are non-negotiable. Call centers often process sensitive information (like payment details and personal identifiers), so it’s critical to choose a provider that meets regulatory standards.

Look for tools with encrypted storage, secure transmission, and clear data retention policies. Strong safeguards reduce legal risk, build trust with your customers, and give teams confidence to use speech data for analytics, training, and AI-driven workflows.

The different types of speech recognition tools

Call centers can choose from several types of speech recognition tools, each built for different needs and use cases. Some focus on speed, others on accuracy and analysis. Understanding these categories can help you choose the solution that best fits your workflow.

• Real-time transcription tools: Convert speech to text instantly, enabling live call routing, voicebot interactions, or real-time agent assistance. These tools prioritize speed and low latency so AI assistants can respond to callers immediately.
• Batch transcription tools: Process recorded customer calls after the conversation ends, creating transcripts for analysis, quality assurance, or training. Ideal for reviewing interactions at scale without the pressure of live processing.
• Speech analytics platforms: Go beyond basic transcription to detect keywords, analyze customer sentiment, and score agent performance. They surface trends in customer experience, flag compliance issues, and help optimize team effectiveness.
• End-to-end contact center platforms with embedded ASR: Bundle ASR directly into cloud contact center platforms. This removes the need for a separate ASR tool and provides a streamlined system that combines voice handling, analytics, and AI-driven workflows in one solution.

Top speech recognition tools for call centers to consider

Call centers have a wide range of speech recognition tools available, many offering real-time transcription, low-latency APIs, and functionality designed for high-volume environments. The right choice depends on your size, complexity, and integration needs. A few providers stand out:

• Google Cloud Speech-to-Text: Delivers robust real-time and batch transcription, multi-language support, and cloud-based workflow integration.
• Amazon Transcribe: Scales easily and includes speaker ID and custom vocabulary, making it useful for complex call center setups.
• Deepgram: Focuses on high accuracy and low latency, often used in automated voicebots and analytics pipelines.
• Speechmatics: Covers a wide range of languages and dialects, making it a strong option for global call centers.
• CallMiner Eureka: Combines transcription with speech analytics, sentiment scoring, and agent performance insights.

These tools can integrate with AI platforms like Rasa Voice, feeding transcripts directly into an assistant’s NLU pipeline to reduce call volumes, improve resolution rates, and support modern self-service workflows.

How to integrate speech recognition with Rasa

When you connect speech recognition to Rasa, caller audio flows into natural language understanding, enabling your assistant to understand the customer’s purpose and handle conversations naturally. The setup is straightforward and follows a few key steps.

Set up your ASR pipeline

Integrating speech recognition with Rasa begins with a reliable ASR pipeline. Connect a speech-to-text tool like Whisper, Deepgram, or Google Speech-to-Text to capture caller audio and convert it into text that Rasa can process.

A well-configured pipeline ensures speech flows smoothly from the caller to the assistant, laying the groundwork for accurate and responsive conversational AI.

Route transcripts into the NLU pipeline

Once speech is converted to text, the transcript feeds into Rasa. Here, the assistant interprets the caller’s intent, extracts relevant details, and generates context-aware responses.

For instance, if a customer says, “I need to check my order status,” the NLU identifies the intent as order tracking and pulls out details like the order number or product name.

Routing transcripts through the NLU pipeline allows your AI assistant to handle both simple requests and multi-step conversations. This ensures voice interactions are processed with the same accuracy and intelligence as text-based chats, keeping the experience consistent across channels.

Support multi-turn voice conversations

Once speech recognition feeds transcripts into Rasa, your AI assistant can manage full conversations through voice, not just single-turn requests. It tracks context, follows the flow of dialogue, asks clarifying questions, and supports complex workflows across multiple exchanges.

For example, a caller might begin with, “I need to update my billing information,” and later add, “Also, can you check my last payment?” Rasa maintains conversation history so the exchange flows naturally without forcing the caller to repeat themselves. This turns your voice assistant into a true conversational partner, ready to handle real-world call center interactions.

See how voice fits into your assistant strategy

Speech recognition gives call centers the ability to capture spoken language, unlock insights, and boost agent productivity while enhancing the customer experience. But transcription is only the beginning. Real impact comes from connecting those transcripts to systems that drive efficiency, consistency, and smarter customer interactions.

By combining speech recognition with Rasa’s NLU and dialogue management, teams can turn raw audio into meaningful interactions. The Rasa Platform helps assistants understand context, automate workflows, and deliver consistent service across both voice and text channels—all while giving teams full control over data and customization.

Ready to take the next step? Connect with Rasa to explore how conversational AI can elevate your call center strategy.