How to make text to speech sound less robotic

You know the moment you hear it. Flat, drawling, oddly uncanny. It's an unmistakeable sound of a robot reading words it doesn't know and doesn't understand. Maybe it was an older text to speech (TTS) model that couldn’t handle human prosody or a default Interactive Voice Response (IVR) system. Either way, it's off-putting.

Beyond feeling unnatural, recent research suggests that robotic TTS voices reduce perceived emotional capacity and trustworthiness. Affective speech in robots helps to build a stronger connection with the listener, improving everything from interaction quality to perceived helpfulness. For businesses looking to deploy TTS at scale, creating a natural-sounding TTS voice is imperative.

In this article, we’ll explore how to make text to speech sound less robotic, breaking down the top reasons why a robotic voice falls flat and outlining the top strategies you can use to create a human-like TTS.

Summary

• Robotic text to speech stems from missing the qualities that make human speech feel natural. These span across pitch, delivery, pauses, and more.
• Modern AI voice models replicate the full range of human expressiveness, including emotion, rhythm, and emphasis.
• You can make text to speech sound less robotic by choosing the right voice, adjusting speed, selecting a high-quality model, and adding punctuation cues.
• Developers can use SSML tags and prosody controls for the most natural output.
• ElevenLabs Text to Speech delivers human-level expressiveness across 70+ languages.

Why does text to speech sound robotic? 

Early text to speech models mainly stitched together individual phonemes to reverse engineer what a word should sound like. While this may seem to work on paper, the actual nuance of human language is far more complicated. 

While the phonemes used to produce the word “better” in IPA are always /bɛt ər/ the total duration that those sounds last depends enormously on the tone and emotion of the word. A sarcastic sentence and a serious one both use the same fundamental building blocks but would use them completely differently.

That’s where early text to speech models got it wrong.

Here are the main factors that make text to speech sound robotic:

• Flat intonation: Intonation is the natural rise and fall of pitch while you speak. Without changing intonation accordingly, TTS delivers a flat, monotone reply that feels droning to the listener.
• Lack of natural pauses: Even if only for a few hundredths of a second, Humans pause before key words, clauses, when moving through punctuation, and to mark the start of a new sentence. If your TTS reader is reading without inserting natural pauses, it will sound strange. 
• Little emotional variance: Over just a few sentences, humans may move through a diverse range of emotions, all of which influence the prosody and tone of their words. Without a contextual understanding of emotion, a TTS system can miss these subtle cues and sound hollow. 
• Unnatural stress patterns: In most languages, stress falls on certain syllables to help clarify their meaning. When working with a robotic TTS system, you’ll miss out on those stress patterns, blurring words and reducing the quality of the recording.
• Mispronunciation of technical terms: Older TTS models frequently trip up on acronyms, proper nouns, names, and sometimes even numbers. For example, saying something like “Apee” instead of spelling out “API” when faced with this acronym. Even one case of this can quickly throw the listener off and signal an unnatural TTS pattern.

Understanding each of these root causes helps point toward the fix. By iteratively improving how a text to speech model handles each of these, you can build a more successful model that moves away from sounding robotic.

How AI changed natural-sounding text to speech

While fixing the five points above might sound fairly simple on paper, the reality is that it’s an enormous undertaking that wasn’t feasible until the rise in access to artificial intelligence. AI systems use neural networks and deep learning to better understand the relationship between text and speech.

AI speech models train on vast pools of data from real human recordings to iteratively build knowledge and refine pronunciation over time. Here is a quick guide to the AI technologies that made that possible:

• Deep learning and prosody modeling: Neural networks train on human speech as a whole, ranging across rhythm, stress, intonation, and inferred meaning. Instead of only focusing on pronunciation, deep learning models construct a better context of how an entire phrase should sound and what it means.

• End-to-end models: Rather than splitting TTS into separate modules (like Grapheme-to-Phoneme and prosody generation), AI models are able to handle the entire process in one pass. Converging all these systems into one helps to reduce latency while also producing a more natural-sounding TTS final output.

By combining these technologies, AI voice generation is capable of expressing emotion and varying pace during a sentence. At scale, this produces AI voices that sound virtually indistinguishable from human recordings.

How to make text to speech sound less robotic

Whether you're planning on publishing an audiobook version of a novel, an educational e-book or guide, or even videos that may require audio translation or a script, prioritizing natural-sounding audio will provide a pleasant listening experience for your audience.

Improving text to speech quality is also a way of enhancing accessibility to audio content, helping to expand your audience while creating equitable content.

There are several ways you can optimize TTS technology to produce a natural-sounding human voice without spending extensive time or resources.

Let's explore some of these strategies below.

Choose a high-quality voice model

Perhaps the most foundational quality to whether or not your text to speech output sounds robotic is the model you use to create it. Voices built on smaller datasets or older synthetic architectures will sound less natural, as they don’t have the same range of knowledge to build upon when producing audio.

When selecting a TTS platform, look for:

• Large, diverse training datasets
• Expressive models, designed for emotion 
• The ability to produce audio across a range of use cases, from narration to conversational speech

A strong model handles this and more without forcing you to compromise on quality. 

Adjust the voice controls

Most modern TTS platforms offer some degree of flexibility in how you configure their voice outputs. If the voice sounds a little too slow or its pitch just isn’t quite right, this is where you make those iterative adjustments to make the voice sound more natural.

While the specific controls vary by platform, ElevenLabs offers the ability to change the output’s speed, stability, similarity, and style. These allow you to fine-tune the pace and expressive qualities of a voice, making the model feel as realistic as possible.

Especially if you want to deploy a TTS agent for a specific use case, playing with these qualities can create a final output that perfectly fits. 

Use Speech Synthesis Markup Language (SSML)

SSML is an XML-based standard that offers precise control over how TTS engines render human speech. When using the ElevenLabs Text to Speech API, you can implement SSML elements to more accurately structure AI agent speech.

Here are some key speech synthesis markup language elements to use:

<speak>
  <!-- Add a pause of 500 milliseconds -->
  <break time="500ms"/>

  <!-- Control speech rate and pitch -->
  <prosody rate="slow" pitch="+2st">
    This needs emphasis.
  </prosody>

  <!-- Spell out an acronym -->
  <say-as interpret-as="characters">API</say-as>

  <!-- Force correct pronunciation -->
  <phoneme alphabet="ipa" ph="ˈtɛknɪkəl">
    technical
  </phoneme>
</speak>

Implementing these tags allows you to control exactly how the TTS software speaks or pronounces certain words. For non-technical users, Eleven v3 allows you to incorporate expressive audio tags to write specific rules into your scripts. Additional information like [sarcastically] or [long pause] builds out contextually rich scripts.

Incorporate rhythm

Although this is often done subconsciously, humans include natural rhythm while speaking. Include prosodic features in your text to speech tools to ensure they produce authentic-sounding narration and replicate real-life conversations.

Rhythm can include variations in pitch and emphasis on specific words or phrases while maintaining a natural speech pace. That said, be careful not to go overboard with this. An audio clip that has a high variance may begin to produce artifacts. 

Consider voice cloning technology

Another way you can take your text to speech platform to the next level is to incorporate your own voice. While ElevenLabs offers an enormous catalog of pre-built voices for you to experiment with, why not take a few minutes to clone your own voice and use it in your products. 

You can either opt for Instant Voice Cloning or Professional Voice Cloning, depending on the final result you’re looking for and how much time you have. Even with the former, you’ll produce a high-quality voice clone that captures your natural way of speaking.

For more information, be sure to check out our deep dive on how to clone your voice.

How ElevenLabs produces natural-sounding AI voices

ElevenLabs Text to Speech uses proprietary voice models trained on professional human audio across dozens of speaking styles, accents, emotions, and scenarios. Our most expressive model to date, Eleven v3, captures the full range of human affect, providing high-quality audio no matter the use case.

A few core factors set ElevenLabs natural TTS from other tools:

• Natural human-like expressiveness: Eleven v3 adapts delivery to the emotional context of your text automatically. By reading and understanding the context of what you’ve written, it conveys the emotions that bring real meaning to your words.
• 70+ languages: Across over 70 languages, Eleven v3 is able to deliver near-native-quality pronunciation. Discover the full list of languages Eleven v3 supports.
• API access: The ElevenLabs Text to Speech API allows you to embed our TTS into your ecosystems. With low-latency, we can power your real-time applications with natural-sounding voices. 
• Voice library: Our extensive voice library lets you browse through hundreds of potential voices, selecting the best possible professional voice to power your systems.
• Integration into the wider ecosystem: Text to Speech is only one part of the ElevenLabs ecosystem. From our extensive range of tools within ElevenCreative to full end-to-end AI agent production with ElevenAgents, we’re here to serve you the best voice AI systems.

Together, these capabilities help provide your business with natural-sounding AI voices.

Get started with ElevenLabs Text to Speech

The fastest way to hear the difference between a robotic TTS model and natural TTS is to try it for yourself. Whether you’re building a full conversational agent to handle customer inquiries or just want quick access to natural-sounding TTS, ElevenLabs has something for you.

ElevenLabs offers studio-quality audio in seconds. Paste in any text, select a voice, and get started. Discover more about the ElevenLabs Text to Speech tool or sign up to get started today.

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