The #1 best way to make Text-to-Speech More Natural

Introduction

Learn how to make text-to-speech sound more natural. Text-to-speech (TTS) technology has improved dramatically in recent years. However, while today’s text-to-speech engines sound much more human-like than their robotic predecessors, there is still room for progress. The goal for many text-to-speech developers is to make TTS sound completely natural, indistinguishable from a real human voice. Achieving truly natural-sounding text-to-speech requires attention to many different factors.

What makes text-to-speech sound natural?

There are several key attributes that contribute to making text-to-speech sound natural:

• Prosody – This refers to the rhythm, intonation, and emphasis placed on words. Human speech has varying pitch, volume, speed, and pausing. Text-to-speech needs to emulate this natural cadence.
• Pronunciation – Every word needs to be pronounced accurately and clearly. Mispronunciations are a dead giveaway that the voice is synthesized.
• Voice quality – A natural voice has smoothness, richness, and warmth. Text-to-speech voices should avoid sounding robotic or choppy.
• Breathing and mouth sound – Little imperfections like breaths between sentences and lip smacks make TTS sound human.
• Emotional tone – Humans reveal emotions through tone. A convincing text-to-speech voice needs to convey joy, sadness, and excitement as appropriate.
• Accents – Unique regional accents and dialects are challenging for TTS. But they add realism.

When these attributes come together, text-to-speech can reach a remarkable level of naturalness.

How to make text-to-speech sound more natural

There are several ways developers and engineers are working to improve the naturalness of text-to-speech voices:

• Recording large volumes of human speech data to train machine learning algorithms. The more data the better.
• Focusing on modeling the intricacies of human prosody through techniques like recurrent neural networks.
• Building more comprehensive pronunciation dictionaries with phonetic transcriptions.
• Synthesizing breaths, mouth sounds, vocal fillers, etc, and inserting them in appropriate places.
• Allowing for pitch, volume, and rate of speech to be modulated throughout output.
• Developing algorithms that analyze sentence structure, meaning, and emotion to determine appropriate delivery.
• Creating personalized voices by learning from a specific speaker’s vocal tendencies.

How to choose the right text-to-speech voice

With all the options available today, how do you select the text-to-speech voice that sounds most natural for your needs? Here are some factors to consider:

• Language and accent – Choose a voice that fits your target audience’s native language and dialect.
• Gender – Male and female voices are available. Pick a gender suitable for your use case.
• Age – Younger and older-sounding voices evoke different reactions. Select an age-appropriate voice.
• Emotion and style – Do you want an energetic, serious, or friendly-sounding voice? Consider the emotion you want to convey.
• Neural versus standard voices – Neural text-to-speech voices synthesized with deep learning sound more human.
• Custom options – Some services will create personalized voices for a fee.

Evaluating voice demos and trials to find the right balance of naturalness and suitability for your needs is key.

The different factors to consider when choosing a text-to-speech voice

Here are some of the top factors to keep in mind when selecting a text-to-speech voice:

• Naturalness – This is the most important factor. The voice should sound human-like, with proper intonation and emphasis.
• Clarity – Ensure the voice is intelligible and each word is distinguishable. Listen for any mumbled or garbled sections.
• Tone – Pick a tone (friendly, serious, cheerful, etc) fitting your use case and audience.
• Speed – The voice should speak at a natural cadence – not too fast or slow. Speed should also be adjustable.
• Language support – Ensure the voice can handle the language, vocabulary, and accent needs.
• Customization – Some services allow you to fine-tune the pronunciation of specific words and phrases.
• Price – While natural voices cost more, make sure the voice still fits your budget constraints.
• License terms – Pay attention to usage limits and integration permissions before licensing a voice.

prioritizing naturalness while also weighing budget, language needs, and customization options will help guide your ideal text-to-speech voice selection.

How to find the right text-to-speech voice for your needs

Finding the perfect text-to-speech voice that sounds natural while meeting your specific use case requirements involves:

• Research – There are many text-to-speech vendors like Amazon, Google, Microsoft, etc. Browse options thoroughly.
• Before you buy, listen – Never select a voice without first listening to voice samples to assess quality.
• Select by language/accent – Match the voice accent to your target audience. Go for regional specificity.
• Tune voice settings – Adjust the speech rate, pitch, etc to maximize naturalness for your case.
• Try different voices – Give a few different voices a test drive before deciding. Comparison helps.
• Consider customization – For specialized vocab, named entities, etc, customized voices can be more natural.
• Read samples aloud – Don’t just listen passively. Read your own content out loud. Hear how it sounds.

Investing time upfront to critically evaluate voices save pain later. Test voices thoroughly across diverse content.

How to adjust the settings of your text-to-speech software

Adjusting the settings in text-to-speech software can help make the voice sound much more natural. Here are some key settings to tweak:

• Pace and speed – Increase or decrease speaking rate to sound more human.
• Pitch – Vary pitch to add intonation. Higher for questions, lower for serious tones.
• Volume – Use volume strategically to emphasize certain words or passages.
• Pauses – Add slight pauses between sentences or paragraphs.
• Vocal tract length – Adjust this to change the perceived age and gender of the voice.
• Pronunciations – Fix any mispronounced words that sound unnatural.
• Emphasis – Stress particular words you want to stand out.
• Intonation – Use rising and falling pitch more dynamically.
• Breathing – Insert periodic breathing sounds for an incredibly natural effect.

Test different combinations of settings while continuously evaluating speech quality until you arrive at your ideal, natural-sounding voice profile.

The different settings you can adjust to make text-to-speech sound more natural

Text-to-speech technology provides various settings you can tweak to help the computer-generated speech sound more human:

• Pace – Increase or decrease words per minute. Faster for excitement, slower for seriousness.
• Pitch – Vary pitch levels and contours to match human speech patterns.
• Range – Expand or compress vocal range for more expression.
• Tone – Choose tone qualities like breathy, relaxed, or crisp to set the mood.
• Volume – Use louder and softer volumes strategically like humans.
• Punctuation – Set how punctuation like commas and periods affect pausing.
• Emphasis – Stress words by increasing the volume or pitch on them.
• Dialogue – Adjust speed, pitch, etc automatically for multi-speaker passages.
• Vocal tract – Change the vocal tract length to alter the timbre and perceived age/gender.
• Emotion – Choose different emotional styles like joyful or somber.

Adjusting these settings in combination allows you to sculpt the most natural-sounding text-to-speech voice.

How to find the right settings for your needs

Finding the optimum text-to-speech settings for your specific use case requires:

• Understanding your goals – What exactly do you want the TTS to sound like? Define the needed style.
• Listening critically – Keep evaluating different setting combos objectively for naturalness.
• Testing with real samples – Use actual content you plan to have read for the best assessment.
• Trying extremes – Experiment with exaggerated settings to better understand the impact.
• Using faces and crowds – For dialogue, test multi-voice settings interactions to avoid unnaturalness.
• Examining analytics – tools like AWS Polly give insights into volume, pitch, etc to guide tweaks.
• Comparing options – Don’t just use system presets. Contrast different custom settings.
• Asking others to listen – Get additional subjective feedback on how natural it sounds.

Refining text-to-speech settings takes trial and error. The goal is the most natural, human-like delivery of actual content.

How to use natural language processing

Natural language processing (NLP) is another technology that can help make text-to-speech sound significantly more human-like and natural. Here is how it works:

• Text analysis – NLP algorithms deeply analyze sentence structure, grammar, tense, etc.
• Meaning extraction – The system determines the precise meaning and sentiment behind the text.
• Applying linguistics rules – With understanding, NLP can apply proper pronunciation, intonation, inflection, etc.
• Emotion detection – It identifies emotional language and delivers text appropriately happy, sad, irritated, etc.
• Intent recognition – Understanding the writer’s intent allows delivering text accordingly – instructional, humorous, descriptive.
• Context awareness – Knowledge of preceding text prevents unnatural, disjointed delivery.
• Conversational modeling – For dialogues, NLP ensures natural, seamless back-and-forth between speakers.

With all these techniques, NLP transforms robotic-sounding text-to-speech into much more eloquent, human-like reading.

How natural language processing can be used to make text-to-speech sound more natural

Natural language processing (NLP) leverages AI to help text-to-speech systems understand text on a deeper, more human level. Here are some of the ways it achieves this:

• Sentiment analysis – Detects writer’s sentiment and emotional context to inform stylistic elements like tone, pace, etc.
• Entity recognition – Identifies entities like people, places, etc to allow for proper name pronunciations.
• Part-of-speech tagging – Tags each word’s grammatical role to guide pronunciation based on context.
• Pragmatic analysis – Interprets text semantics and pragmatics to derive appropriate emphasis and intonation.
• Discourse analysis – Understands the high-level structure and logical flow of text passages.
• Inferencing abilities – Makes logical inferences about unstated implications in text.
• Conversational context – For dialogues, keeps track of conversation history and relationships between speakers.
• Linguistic variation – Detects text language and dialect to handle divergent rules of pronunciation, grammar, etc.

By implementing NLP techniques like these, text-to-speech systems can achieve far greater mastery of language nuance and thereby deliver much more human-like speech.

How to find a text-to-speech software that uses natural language processing

If you want to take advantage of natural language processing for more natural text-to-speech, here is how to select capable software:

• Look for TTS specially touting “NLP” – this indicates it directly incorporates NLP capabilities.
• Seek out “neural” and “AI” driven text-to-speech – these leverage machine learning implicitly including NLP.
• Investigate sentiment analysis features – this requires deep NLP understanding.
• Verify pronunciation of slang and acronyms – only NLP can handle these properly.
• Confirm it handles multiple languages – NLP parses different language rules.
• Check for conversational abilities – NLP powers coherent discourse between voices.
• Review voice samples for nuanced delivery – evidence NLP is interpreting meaning and emotion.
• Compare multiple NLP-enhanced options – no standard for NLP implementation yet.

Prioritizing NLP-enabled text-to-speech will ensure the most natural-sounding voice with the full meaning and context of the text intact.

Conclusion

Achieving natural-sounding text-to-speech that can pass for human speech remains an ongoing research challenge. But steady progress has been made leveraging big datasets, neural networks, comprehensive linguistics analysis, and advanced customization. Heading into the future, expect text-to-speech systems to better model the intricate complexities and nuances of natural language and human vocalization. With sufficient data and computing power, the gap between real and synthesized voices will ultimately become indiscernible. For any applications requiring an artificial voice, invest time upfront to critically listen to samples and tune settings for optimal quality. Naturalness should be the top priority. With the right voice and the right tuning, you can make your text-to-speech application sound incredibly human.

The Future of natural text to speech

Looking ahead, we can expect the naturalness of text-to-speech voices to improve in several ways:

• More robust datasets will enable the training of more advanced AI models.
• New techniques like generative adversarial networks (GANs) will enhance voice realism.
• Personalization will allow custom voices built from your own speech patterns.
• Real-time emotion modulation will reflect changing sentiments.
• Greater computational power will drive multimodal synthesis incorporating video.
• Tighter integration with natural language processing will improve context handling.
• Wider linguistic and accent support will remove language barriers.
• Enhanced customization tools will increase adoption.
• Falling costs and improving quality will expand applications.

The future of truly natural text-to-speech is bright. Expect the technology to become ubiquitous across devices and platforms as it reaches human parity.

How to get started with natural text to speech

Getting started with more natural-sounding text-to-speech is easy:

• Research the most natural-sounding voices in your language. Focus on neural options.
• Use free trials to test out voices across diverse sample content.
• Make use of voice customization tools to tailor existing voices.
• If available, utilize NLP-enabled text-to-speech for added naturalness.
• Tweak speed, pitch, volume, and other voice settings for optimal quality.
• For dialogues, leverage multi-voice options with conversational ability.
• Consider a custom voice if suitable for your use case.
• Retest periodically as technology rapidly evolves.

With a thoughtful selection process and tuning, you can find a text-to-speech voice that sounds incredibly human for use across applications. Evaluating voices on samples of your own content is key to achieving a natural end result.

Text-To-Speech Tools

Some popular Text-to-Speech Tools to consider.

1. Wellsaid Labs Ai –>( Read Review )
2. Eleven Labs Ai –>( Read Review )
3. Murf Ai –>( Read Review )
4. Speechify –>( Read Review )
5. PlayHT –>( Read Review )
6. DupDub –>( Read Review )
7. Listnr –>( Read Review )
8. Uberduck AI  –>( Read Review )
9. Descript –>( Read Review )
10. Lovo Ai –>( Read Review )
11. Verbatik –>( Read Review )
12. Resemble Ai –>( Read Review )