What is speech-to-speech AI? How <250ms latency changes customer conversations

Key takeaways

1. Speech-to-speech AI removes unnecessary processing steps to create faster and more natural voice conversations.

2. Response times below 250 milliseconds help conversations feel more human, improving customer engagement and reducing interruptions.

3. Organisations adopting low-latency voice AI can improve customer satisfaction, agent productivity and operational efficiency.

4. High-quality infrastructure, optimised models and reliable network performance are essential for achieving real-time voice interactions.

5. Tata Communications Kaleyra™ combines enterprise communications expertise with conversational voice capabilities to help organisations deliver responsive customer experiences.

Voice conversations have become one of the most important ways for businesses to engage customers. Whether someone is booking an appointment, checking an account balance or contacting customer support, they expect conversations to feel as natural as speaking with another person. Long pauses, delayed responses and awkward interruptions quickly affect confidence and satisfaction.

This is why response time has become just as important as conversation quality. Traditional voice systems often introduce delays because speech must pass through several processing stages before a response is generated. New speech-to-speech AI technology is changing that approach. By reducing processing time and enabling near-real-time conversations, organisations can deliver faster, smoother and more natural customer experiences at scale.

The traditional voice AI pipeline: ASR → LLM → TTS

Most conversational voice systems available today follow the same basic process.
First, spoken words are converted into text using Automatic Speech Recognition, commonly referred to as ASR. That text is then analysed by a language model to determine the appropriate response. Finally, the response is converted back into spoken audio using Text-to-Speech technology. Although each stage happens quickly, every processing step introduces a small delay. When combined, these delays often become noticeable during conversations.

• The customer finishes speaking

• The system pauses

• Processing begins

• A response is generated

• Only then does the customer hear an answer

While the delay may only last a fraction of a second, it is enough to interrupt the natural rhythm of a conversation.

Where does latency come from?

Latency refers to the time taken between a customer finishing a sentence and hearing a response. In traditional voice systems, several factors contribute to this delay. Some of the most common include:

• Speech recognition processing: Audio must first be converted into accurate text before the conversation can continue.

• Language model processing: The system analyses the customer's request, understands the context and generates an appropriate response.

• Speech synthesis: The generated response is converted back into spoken audio.

• Network transmission: Audio travels between the customer, cloud infrastructure and processing services.

• Business application lookups: Customer records, knowledge bases or CRM systems may also need to be accessed before the response is completed.

Each individual task adds only a small amount of time. Together, however, they often create noticeable pauses that interrupt the flow of conversation.

Why 800ms+ latency kills customer experience

People naturally expect conversations to move quickly. If somebody pauses for too long during a face-to-face discussion, the silence immediately feels uncomfortable. The same happens during customer service interactions. When response times exceed approximately 800 milliseconds, customers often begin speaking again because they assume the system has not heard them.

This creates several problems.

• Conversations overlap

• Customers repeat themselves

• Agents need to clarify information

• Interactions become longer and more frustrating

These interruptions are particularly noticeable during customer support, appointment booking, banking enquiries and sales conversations where speed and accuracy influence customer confidence.

For businesses, higher latency often leads to:

• Lower customer satisfaction

• Longer average handling times

• Increased call abandonment

• Higher operational costs

Reduced trust in automated customer engagement.

Reducing latency therefore improves more than speed. It creates conversations that feel smoother, more responsive and significantly closer to natural human interaction.

What is speech-to-speech AI (S2S)?

Traditional conversational systems process speech in several separate stages. Speech-to-speech AI takes a different approach. Instead of repeatedly converting speech into text and back into speech, a speech-to-speech model is designed to understand spoken language and generate spoken responses much more directly.

The objective is simple.

• Reduce unnecessary processing

• Shorten response times

• Create conversations that feel immediate

This approach enables AI voice-to-voice interactions that are considerably more fluid than traditional conversational systems. Rather than waiting for multiple processing stages to complete, customers experience conversations that continue naturally with minimal interruption.

How S2S eliminates intermediate processing steps

One of the biggest advantages of speech-to-speech AI is its ability to reduce the number of separate processing stages involved in each interaction.

Rather than treating speech recognition, language understanding and voice generation as completely independent tasks, the technology creates a far more streamlined workflow.
The benefits include:

• Reduced processing time: Fewer intermediate steps help generate responses more quickly.

• More natural conversations: Shorter pauses allow discussions to flow more like human conversations.

• Improved conversational context: Because less information is repeatedly transformed between formats, conversations remain more coherent.

• Lower overall latency: Faster processing helps organisations achieve the responsive interactions expected from modern customer service.

For organisations deploying a modern voice AI platform, this translates into conversations that feel less like interacting with software and more like speaking with a knowledgeable representative.

S2S vs traditional TTS: Sound quality comparison

Speed alone does not create a better customer experience. The quality of the spoken response is equally important. Traditional Text to Speech systems often produce speech that sounds clear but can sometimes feel overly structured or mechanical, particularly during longer conversations.

By comparison, modern AI voice-to-voice technologies aim to preserve more natural speech characteristics, including rhythm, pacing and conversational flow. The differences become particularly noticeable during complex customer interactions where the conversation changes direction, includes follow-up questions or requires empathy.

As organisations continue investing in real-time voice AI, improvements in both speed and speech quality will become essential for delivering customer experiences that feel natural rather than automated.

Why <250ms response time is the new enterprise standard

Fast response times are no longer a 'nice to have'. They have become essential for delivering natural customer conversations and improving business performance.

Human conversational norms: What science says

People expect conversations to flow naturally, with very little delay between one person speaking and the other responding. When voice systems pause for too long, customers often interrupt, repeat themselves or assume the conversation has ended. A response time of less than 250 milliseconds helps create a smoother experience by:

• Making conversations feel natural: Responses closely match the pace of human conversations.

• Reducing interruptions: Customers are less likely to speak over the system or repeat their questions.

• Improving engagement: Faster replies keep conversations flowing without awkward pauses.

• Building customer confidence: Quick responses reassure customers that their requests have been understood.

Business impact of low-latency voice AI

For organisations using low-latency voice AI, faster responses improve both customer experience and operational efficiency. Key business benefits include:

• Shorter call durations: Conversations progress more efficiently with fewer delays.

• Higher customer satisfaction: Faster interactions create a better overall experience.

• Improved agent productivity: Agents spend less time clarifying information and more time resolving issues.

• Higher call completion rates: Customers are more likely to remain engaged throughout the conversation.

• Greater operational efficiency: Businesses can manage larger interaction volumes while maintaining consistent service quality.

Technical requirements for sub 250ms voice AI

Achieving response times below 250 milliseconds requires much more than a fast conversational model. Every part of the technology stack contributes to overall performance, from network connectivity and infrastructure to processing architecture and application design.

1. Network infrastructure

Every time a customer speaks, information travels through networks before a response is generated. The greater the physical distance between users and processing infrastructure, the longer this journey becomes.

Edge computing helps reduce these delays by processing customer interactions closer to where they originate. This approach provides several advantages:

• Reduced network travel time: Processing closer to users lowers overall latency.

• Faster response delivery: Customers receive answers more quickly during live conversations.

• Improved reliability: Distributed infrastructure helps maintain consistent performance during periods of high demand.

• Better scalability: Organisations can support growing customer interaction volumes while maintaining responsive service.

Reliable network infrastructure also plays an important role. Stable connectivity, sufficient bandwidth and efficient routing all contribute to delivering consistent low-latency voice AI experiences.

2. Model architecture optimisations

Infrastructure alone cannot achieve real-time conversations. The conversational model itself must also be designed for speed and efficiency. Modern speech-to-speech models incorporate a range of optimisation techniques to reduce processing delays while maintaining conversation quality.
Important considerations include:

• Efficient model design: Optimised architectures reduce the time needed to understand customer requests and generate responses.

• Parallel processing: Multiple tasks can be completed simultaneously instead of sequentially, improving overall responsiveness.

• Resource optimisation: Efficient use of computing resources helps maintain performance during periods of heavy demand.

• Continuous optimisation: Regular model improvements support better accuracy and lower latency over time.

When combined with robust infrastructure, these optimisations enable organisations to deliver responsive customer experiences across a wide range of business scenarios.

Use cases where latency is mission critical

Not every customer interaction requires an instant response. However, there are situations where even a short delay can affect customer confidence, decision-making, or service quality.
These are the environments where real-time voice AI delivers the greatest value.

1. Real-time agent assist

   Customer service representatives often need to access information while continuing a conversation. A modern AI voice agent can provide suggested responses, retrieve relevant customer information and recommend knowledge articles during live calls.

   When these recommendations appear almost instantly, agents can continue speaking naturally without interrupting the conversation. This helps reduce average handling times while improving customer satisfaction.

2. Live fraud detection

   Financial institutions frequently need to identify unusual activity while customers are still on the call. Low-latency voice processing enables systems to analyse interactions quickly, identify potential risks and alert agents in real time.

   Faster responses allow organisations to take appropriate action before fraudulent activity progresses further, helping improve both customer protection and operational efficiency.

3. Medical triage

   Healthcare providers often manage enquiries where every second matters. Patients contacting healthcare services expect immediate guidance, particularly when describing urgent symptoms or seeking advice outside normal consultation hours.

   A responsive voice AI platform can gather information, ask relevant follow-up questions and direct callers to the appropriate level of care without introducing unnecessary delays. While these systems do not replace healthcare professionals, they can support faster initial assessments and improve access to appropriate services.

Across these use cases, the common requirement is clear. Conversations must feel immediate, accurate and dependable. As customer expectations continue to evolve, response time is becoming just as important as conversational intelligence itself.

Why Tata Communications Kaleyra™ is advancing real-time voice conversations

As customer expectations continue to rise, businesses need voice solutions that deliver fast, natural and reliable conversations. Tata Communications Kaleyra™ helps organisations modernise customer engagement by combining conversational voice capabilities with enterprise-grade communications infrastructure. The platform enables businesses to automate routine enquiries, support live agents and manage customer interactions at scale while maintaining a seamless customer experience.

With an advanced voice AI platform, Tata Communications Kaleyra™ integrates with existing CRM systems and business applications, making it easier to streamline operations without disrupting current workflows. An intelligent AI voice agent understands customer intent, responds naturally and transfers complex conversations to human representatives whenever needed. Built for enterprise scale, the platform supports growing interaction volumes while maintaining performance, security and reliability. By bringing together intelligent automation and trusted communications expertise, Tata Communications Kaleyra™ helps organisations improve customer satisfaction, increase operational efficiency and deliver more responsive voice experiences.

The future of speech-to-speech AI in enterprise CX

Several trends are likely to shape the future of voice-to-voice AI across enterprise customer experience.

These include:

• More natural conversations: Improvements in conversational models will make interactions increasingly fluid and human-like.

• Greater multilingual capabilities: Businesses will be able to support customers across more languages while maintaining consistent conversation quality.

• Closer collaboration between humans and AI: Intelligent voice systems will continue assisting agents rather than replacing them, helping customer service teams resolve enquiries more efficiently.

• Wider enterprise adoption: Industries including banking, healthcare, retail, travel and telecommunications are expected to expand the use of conversational voice technology across additional customer journeys.

• Faster response times: Continued investment in low-latency voice AI, edge infrastructure and model optimisation will further reduce delays, creating conversations that feel almost instantaneous.

For organisations planning future customer engagement strategies, conversational speed will become just as important as conversational intelligence.

Explore how Tata Communications Kaleyra™ helps organisations deliver responsive voice experiences through conversational intelligence, enterprise-grade communications and scalable customer engagement capabilities. Schedule A Conversation