What is Text-to-Speech vs. Speech-to-Speech in Voice AI? Pros & Cons

The rise of voice AI is redefining how businesses and contact centres handle customer support - especially over the phone.

With automation improving by the day, many contact centres are turning to AI voicebots to automate more queries, improve routing, reduce wait times, and provide smoother self-service.

But not all voice AI is created equal.

Behind the scenes, different voice technologies power these bots. The most common are text-to-speech (TTS) and the more emerging speech-to-speech (STS).

Both approaches enable AI customer service over the phone, but they work in different ways - each with its own strengths and limitations.

Whether you're exploring voice automation for the first time or looking to upgrade from traditional IVRs, it's important to understand the differences between these technologies.

In this article, we'll break down how text-to-speech and speech-to-speech technology works, explore their pros and cons, and help you make the best choice for your business.

We'll cover:

• What is voice AI in customer support?
• What is text-to-speech technology?
• What is speech-to-speech technology?
• The pros and cons of text-to-speech and speech-to-speech
• TTS vs STS: Which is better for voice AI customer service?

TL;DR:

Text-to-speech (TTS) and speech-to-speech (STS) are two key technologies powering AI voicebots for customer support.

• TTS is flexible, scalable, and cost-effective, with extensive support for multiple languages, accents, and AI models - making it ideal for large-scale, structured automation.
• STS offers more expressive, emotionally intelligent conversations, with faster, more natural interactions - but comes with higher costs, fewer customisation options, and limited language/model flexibility.

The best choice depends on your business needs, goals, and customer service use cases.

What is AI Voice in customer support?

In customer support, voice AI refers to artificial intelligence systems that can understand, process, and respond to spoken language in real time.

This enables natural, human-like conversations over the phone between customers and call centre voice AI systems.

In turn, the AI can answer customer queries and automate tasks (e.g. booking appointments, retrieving order status updates) in a seamless, human-like way.

Unlike legacy IVR systems that rely on rigid menus and pre-recorded prompts, modern AI voicebots use technologies like speech recognition, natural language processing (NLP), generative AI, and speech synthesis to engage customers dynamically and intelligently.

These conversational AI voicebots can interpret what callers say, determine intent, and deliver personalised, spoken responses - all without involving a human agent.

It’s this advanced interaction model that allows voice AI to act as a true virtual assistant, transforming how businesses handle phone support.

Behind the scenes, AI voice systems typically use a combination of:

• Automatic Speech Recognition (ASR): Converts input speech into text.
• Natural Language Processing (NLP): Performs linguistic analysis to understand intent, context, and meaning - even across different langauges, voices, and accents.
• Large Language Models (LLMs): Use deep learning techniques and neural networks to generate contextually relevant responses.
• Text-to-Speech or Speech-to-Speech: Converts those responses into spoken voice output - enabling smooth, two-way communication.

Solutions like Talkative’s Voice AI allow contact centres to automate more calls, improve self-service, integrate AI with their knowledge bases, and seamlessly route conversations to human agents when needed.

And with features like low-latency audio, multilingual capabilities, and the ability to handle overlapping speech, customers enjoy a smoother, more natural-sounding experience.

Whether it’s for improving accessibility, reducing call volumes, or enhancing CX, voice AI is quickly becoming a powerful tool for contact centres looking to scale phone support without sacrificing quality.

What is text-to-speech technology?

Text-to-speech is a form of speech synthesis that converts written text into a voice output.

Originally developed as an assistive technology to help people with visual impairments or reading disabilities hear written content read aloud, TTS is now widely used across customer service, virtual assistants, content creation, and more.

When used in voice AI for customer service purposes, TTS typically works in a multi-step process:

1. The customer speaks, and the system uses automatic speech recognition (ASR) to convert the input speech into written text.
2. That text is then analysed using NLP and language models to determine the best response.
3. TTS technology transforms the text into a natural-sounding voice output, which the customer hears in real time.

What is speech-to-speech technology?

Speech-to-speech (STS) is an emerging form of voice AI that allows systems to generate responses directly from a user’s input, without relying on text-based processing.

Instead of converting speech into text and then back into audio (as with text-to-speech), STS uses deep learning techniques to analyse the spoken input, interpret intent, and produce new outputs using audio alone - often preserving tone, rhythm, and even emotion.

This makes STS ideal for producing more human-like, natural-sounding speech - especially in real-time conversations.

Some systems even support voice conversion, allowing the bot to replicate specific voices or maintain natural dialogue across different languages.

While still a developing technology, STS is increasingly being explored for its potential to deliver more humanised, emotionally intelligent interactions in customer support use cases.

Pros of text-to-speech

Text-to-speech has become a staple of modern voice AI systems - and for good reason.

In this section, we'll cover the key benefits of TTS systems.

1. Highly customisable voice options

One of the biggest advantages of TTS is the ability to choose from a wide variety of voices - including different genders, accents, and tones.

Most modern TTS systems offer extensive voice libraries, enabling businesses to select a voice that aligns with their brand personality.

Whether you need a calm, professional voice for financial services or a warm, helpful tone for healthcare, TTS gives you full control over how your AI sounds.

Some platforms also offer voice cloning or custom voice synthesis, allowing you to create a unique, branded AI voice that reflects your organisation’s identity.

You can even adjust parameters like speech speed and intonation to further tailor the experience - helping to create more natural-sounding speech that resonates with customers.

2. Extensive multilingual & multiregional support

In addition to different voices, text-to-speech typically supports many languages, including different dialects and regional variations.

This makes it an excellent technology for businesses with a global or diverse customer base.

Whether you're serving customers in the UK, Europe, North America, or beyond, TTS systems enable your AI voice to speak fluently in multiple languages - often with native-level pronunciation.

Many TTS platforms also allow businesses to choose from a variety of voices within each language, ensuring that tone and style align with customer expectations across regions.

For example, you might use a warm, conversational voice for UK callers and a more formal tone for enterprise customers in Germany or Japan.

This level of flexibility is particularly valuable for contact centres with international audiences, public-facing organisations that need to make public announcements in more than one language, or businesses aiming to improve accessibility.

3. Supports a range of Large Language Models (LLMs)

One of the key advantages of TTS-based voice AI is its modular architecture, which gives you the freedom to choose the language model that powers your bot’s intelligence.

Whether you prefer OpenAI’s GPT models, Anthropic’s Claude, Google Gemini, or Meta’s Llama, TTS systems can integrate with a variety of LLM providers.

This flexibility makes it easier to optimise your voicebot for different use cases, industries, and customer needs.

You can run A/B tests across models to ensure optimal performance and even switch LLM providers as new models or innovations emerge.

4. Cost-effective & scalable

Compared to STS systems, text-to-speech is far more cost-efficient - especially at scale.

TTS engines are lightweight, cloud-optimised, and often offer various pricing options, making them easy to budget for even in high-volume contact centres.

Since the speech synthesis is separated from the language model, you can also optimise infrastructure and reduce compute demands - keeping costs low while maintaining quality.

TTS also scales effortlessly across many devices, channels, and languages without requiring additional training or compute-intensive processing.

Whether you’re supporting thousands of customer calls per day or expanding into new markets, TTS allows you to grow your voice AI deployment without significantly increasing technical or operational overhead.

Ultimately, this makes TTS a powerful tool for businesses looking to automate customer service, improve accessibility, and enhance communication - all while maximising ROI.

5. Flexible data storage & localisation

With text-to-speech technology, businesses have more flexibility over where and how their interaction and customer data is stored.

This is an increasingly important consideration for organisations with strict data sovereignty or compliance requirements.

Unlike many STS systems, which often rely on centralised models hosted in the US, TTS-based voice AI can be deployed using region-specific infrastructure.

This allows companies to store interaction data, recordings, and other sensitive information in compliance with regional regulations or internal privacy policies.

For business in the UK or EU and highly regulated industries such as healthcare, finance, or public services, this level of control can make all the difference when it comes to secure, compliant automation.

Cons of text-to-speech

While text-to-speech is a beneficial and widely adopted technology in voice AI, it’s not without limitations.

In this section, we’ll explore some of the key challenges to be aware of when using TTS in customer support environments.

1. Limited expressiveness, emotion, & empathy

While text-to-speech technology has advanced significantly in recent years, it can still struggle to match the emotional nuance and conversational rhythm of real human speech.

Even with advanced AI powering more human-like voices, TTS outputs fall short in expressiveness and empathy - especially during emotionally charged interactions.

This lack of emotional range can impact customer satisfaction in scenarios where tone and empathy are crucial, such as complaint handling, healthcare, or crisis support.

Unlike STS systems, which can mimic intonation and emotional inflexion, TTS relies on preset voice models that often lack spontaneity or dynamic variation.

As a result, while TTS is ideal for clear and consistent communication, it may fall short or feel robotic when a specific sentiment is needed to build trust or reassure a customer.

2. Higher real-time response latency

Text-to-speech systems can introduce slight delays during live conversations - particularly when responses need to be generated on the fly.

Because TTS voicebots rely on multiple sequential processes - including speech recognition, natural language processing, response generation via a large language model, and then speech synthesis - the total turnaround time can be longer compared to STS systems.

This delay, even if just a second or two, can make the conversation feel less natural - especially during fast-paced exchanges in high-speed support environments where timing is everything.

3. Less adaptive to natural speech dynamics

While text-to-speech systems are excellent at generating clear, consistent voice output, they can fall short when faced with the unpredictability of human conversation.

TTS engines typically process language in complete sentences or blocks, which means they sometimes struggle with things like barge-in handling (where the user interrupts the bot mid-flow).

In addition to interruptions, hesitations and the messy, overlapping dialogue that often happens in real conversations can also be a problem.

This can make interactions feel less human or rigid, particularly in high-emotion or fast-paced support scenarios.

Pros of speech-to-speech

While still an emerging technology, speech-to-speech offers some compelling advantages for businesses aiming to deliver more human-like automated support.

Below, we dive into the key benefits that set STS apart from more traditional TTS systems.

1. More expressive, emotive, & empathetic

One of the standout advantages of speech-to-speech technology is its ability to deliver far more natural speech than traditional TTS systems.

Because STS produces voice directly from the customer’s input - without relying solely on text conversion - it can replicate subtle vocal cues like tone, emotion, rhythm, and inflection with remarkable realism.

This makes STS ideal for customer service scenarios where empathy and nuance matter.

Whether it’s defusing frustration, expressing reassurance, or responding with appropriate warmth, STS voice output can feel more human and emotionally intelligent than pre-set, TTS voices.

By capturing the expressive qualities of human speech, STS helps voice AI bots move toward truly conversational, human communication - improving customer experience and building trust in ways that TTS systems often can’t.

2. Faster response times

While TTS systems must convert speech to text, then produce a response and convert it back into audio, speech-to-speech technology streamlines this process - often leading to faster, more fluid interactions.

By processing spoken language directly and then instantly producing audio output, STS reduces the number of stages in the response pipeline.

In some cases, it can even begin generating the reply before the full response is finalised, resulting in a more seamless real-time experience.

This low-latency design makes STS especially well-suited to high-speed customer conversations, where timing plays a big role in perceived quality.

By eliminating delays between a customer's input and the AI voice reply, STS helps voicebots feel more responsive and conversational.

3. Better handling of human speech patterns

Speech-to-speech (STS) systems are designed to handle the natural messiness of human conversation - such as overlapping speech, stutters, pauses, and unexpected turns in phrasing.

Unlike TTS-based bots, STS models are trained directly on spoken audio, allowing them to respond more intuitiviely to how people actually speak.

That includes mid-sentence interruptions, background noise, and ambiguous intent - all of which are common in real-world contact centre calls.

By analysing not just the words, but also the tone, timing, and acoustic patterns of human speech, STS enables more responsive and context-aware interactions.

This makes the technology especially useful for applications like live phone support, where understanding nuance is essential and the conversation can change direction quickly.

The result? Smoother, fluid dialogue that mirrors how humans communicate more closely.

4. Seamless multilingual support

Although still a developing technology, STS is increasingly supporting real-time multilingual interactions - often with more fluidity than traditional TTS systems.

Because STS models process audio directly, some can perform voice conversion across different languages, allowing the bot to respond in the caller’s language while preserving the tone, pacing, and even vocal style of the conversation.

This capability enables more natural-sounding speech in cross-language scenarios, such as switching between English and Spanish mid-call, without jarring changes in voice output.

While TTS offers broader language coverage overall, STS holds an edge when it comes to producing more human-like, bilingual or multilingual exchanges.

It's especially promising for global contact centres that want to provide multilingual support while maintaining conversational performance, emotional nuance, and voice consistency.

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Cons of speech-to-speech

While speech-to-speech brings impressive advancements in speech realism and responsiveness, it’s still a relatively new and evolving technology - and that comes with trade-offs.

Next, we'll detail the main challenges that businesses considering STS-powered voice AI should be aware of.

1. Fewer languages supported

While speech-to-speech (STS) technology shows promising multilingual potential, it currently supports far fewer languages and regional accents than TTS systems.

Most STS models are trained on a narrower range of audio datasets, limiting their ability to support accurate, high-quality outputs in less common or lower-resource languages.

For global brands, this can create gaps in coverage - especially when compared to TTS, which often supports 100+ languages and regional dialects.

While seamless multilingual voice conversion and cross-language capabilities are emerging in some STS models, they’re still relatively early-stage - making TTS the more reliable choice currently for businesses that need extensive multilingual capabilities across regions.

2. Lack of voice customisation options

Unlike TTS systems, which offer a broad library of high-quality voices, most speech-to-speech platforms offer very limited control over the way the AI sounds.

Because STS systems are often end-to-end - generating both the content and the voice output from a unified model - users typically can’t choose or customise the voice in the same way they can with TTS.

This means it’s much harder to align the bot’s voice with your brand identity or tailor the tone for specific use cases.

Advanced voice options like brand-trained voices or accent selection are still uncommon in current STS deployments.

And, while some systems support voice conversion (e.g. replicating a specific speaker’s voice), these features are usually experimental and not yet widely available for enterprise use.

As a result, businesses may find STS limiting if they need full control over how their AI voice sounds - especially in industries where brand voice, tone, and CX consistency are critical.

3. Limited choice of Large Language Model (LLM)

Unlike text-to-speech systems, which allow you to choose between various LLMs, most speech-to-speech platforms are built around tightly integrated, end-to-end models.

This means you can’t easily choose or switch the AI brain behind your voicebot.

The response generation, intent understanding, and voice output are all fused into a single pipeline - limiting your ability to experiment, optimise, or customise the experience using your preferred language model.

For teams that want to test different LLMs, adjust prompt strategies, or align with internal tools like AI knowledge bases or CRM systems, this lack of flexibility can be a blocker.

It also makes it harder to future-proof your solution as new LLM providers emerge or as your business needs evolve.

In contrast, TTS systems offer far more modular architecture, enabling greater flexibility, adaptability, and innovation in how you build and evolve your voice AI experience.

4. Data sovereignty constraints

One of the key limitations of many speech-to-speech solutions is their reliance on centralised infrastructure - often hosted in the United States.

For businesses operating in highly regulated industries or regions with strict data sovereignty requirements, this poses serious challenges.

Unlike text-to-speech systems, which can often be deployed using region-specific cloud environments or even on-premise solutions, most STS models require interaction data to be processed and stored on the vendor’s servers.

This limits your ability to maintain control over where sensitive data is stored and how it’s handled -  increasing compliance risk under frameworks like GDPR or the EU AI Act.

For organisations that need to retain local hosting, manage their own data pipeline, or ensure customer communication stays within a particular region, TTS remains the more flexible and secure choice.

5. More expensive to deploy

Speech-to-speech systems are typically more computationally intensive than text-to-speech, making them more expensive to deploy - especially at scale.

Because STS combines multiple AI processes (speech recognition, natural language understanding, and speech synthesis) into a single pipeline, it often requires specialised models as well as higher compute power to run in real time.

That means increased infrastructure costs compared to more lightweight, modular TTS systems, which can often be run efficiently on standard cloud services.

For many organisations, this means STS is currently better suited to premium, high-touch support experiences, rather than large-scale automation where cost-efficiency is a top priority (although this may change in the future as the technology advances).

Text-to-speech vs speech-to-speech: Which is better for AI voice bots?

When it comes to building effective AI voice chatbots for customer support, both text-to-speech and speech-to-speech offer compelling benefits - but they serve different purposes depending on your goals, use cases, and industry requirements.

TTS is the more mature, widely adopted option.

It offers broad language support, flexible voice customisation, and seamless integration with a wide range of LLMs.

It’s also more cost-effective to deploy at scale, with strong options for data localisation, making it ideal for contact centres prioritising control, affordability, and consistent performance across multiple regions and channels.

In contrast, STS delivers a more human, emotionally expressive experience.

It excels in real-time responsiveness and conversational flow - making it better suited for use cases where fluid interactions, empathetic tone, or emotive expressiveness are critical.

However, STS comes with trade-offs.

It offers less language and voice variety, fewer options for customising the underlying AI model, and higher infrastructure demands, which may make it a less practical choice for some organisations - particularly those with strict data sovereignty needs or limited budget and technical resources.

Ultimately, the best solution isn’t one-size-fits-all:

• If your priority is scalability, flexibility, cost-effectiveness, and broad multilingual support, TTS is likely the right fit.
• If you're aiming to push the boundaries of realism, emotion, and human-like performance, STS might offer the edge you're looking for.

Ultimately, the right voice AI approach depends on your industry, customer expectations, and operational goals - and in many cases, the most effective solutions may combine both technologies to get the best of both worlds.

The takeaway

Both text-to-speech and speech-to-speech voice AI have their unique strengths and limitations.

Ultimately, the right choice depends on your organisation’s goals, use cases, and the type of customer experience you want to create.

But no matter which route you take, your success with voice AI also depends on having the right provider.

That’s where Talkative comes in.

With Talkative’s Voice AI, you don’t have to choose between TTS and STS. Our platform supports both, giving you the freedom and flexibility to tailor the solution to your needs.

With Talkative's voice AI, you’ll also benefit from:

• Human-like conversations from the first "hello" - with support for stutters, overlapping speech, and extended silences.
• Multilingual support and customisation - select from a library of voices, languages, accents/dialects.
• Seamless agent handoffs - real-time transcripts and full context transferred directly to your agents.
• Instant, accurate responses - powered by your own private knowledge bases and real-time data lookups.
• Data sovereignty and compliance - keep your customer data where it needs to be.
• Live analytics and performance tracking - including AI call summaries and sentiment monitoring.
• Low-code control - easily build, update, and manage call flows with AI prompts.
• Voice AI Copilot - give agents real-time support during calls and supervisors post-call insights.

Whether you're automating high volumes of routine calls or delivering more human-like conversations and seamless self-service in real time, Talkative provides the tools to help you do it - without compromise.

Want to learn more or see our solution in action?

Reach out to us with any questions or book a demo today.