How AI Voices Differ from Natural Voices

An Artificial Voice (AI) voice and a natural (human) voice have several differences, primarily stemming from the way they are generated and their inherent qualities. 

So, how is an AI voice different from a natural voice? In this blog, I will look at the key differences between the two:

In this article

1. Generation
2. Flexibility
3. Emotional nuance
4. Imperfections
5. Adaptability
6. Creativity
7. Personalization
8. How are AI Voices created?
9. What will it take to make AI voices more like natural voices?
10. Vocal Imperfections That Make Speech Sound Natural

Generation

AI voices are generated using sophisticated algorithms and machine learning techniques, whereas natural voices are produced by the human vocal apparatus (lungs, larynx, vocal folds, and articulators).

Flexibility

Human voices are highly adaptable and can vary in pitch, tone, and emotion based on context and intention. While AI voices have improved significantly in recent years, they may still lack the same degree of expressiveness and naturalness as a human voice.

Emotional nuance

Human voices carry emotional subtleties and cues that convey feelings, intentions, and attitudes. AI-generated voices can emulate emotions to some extent, but they may not be as rich or genuine as human expressions.

Imperfections

Natural voices have imperfections such as stutters, hesitations, and breath sounds that make them unique and human-like. AI-generated voices tend to be more polished, which can sometimes make them sound less natural.

Adaptability

Human voices can effortlessly switch between different languages, accents, or dialects, and adapt to various social contexts. AI voices can be trained to mimic different languages and accents, but they may not be as adaptable or context-aware as human speakers.

Creativity

Human voices can engage in creative wordplay, like puns or jokes, or use metaphors and analogies to convey complex ideas. AI-generated voices can be programmed to understand and generate some forms of humor and wordplay, but their creativity may be limited by their training data and algorithms.

Personalization

Each human voice is unique, with its own timbre, pitch, and speaking style. While AI voices can be designed to mimic a wide range of vocal characteristics, they may not capture the same level of individuality as a natural voice.

Despite these differences, AI-generated voices continue to improve in quality and naturalness, bridging the gap between them and natural human voices. With advances in AI research and technology, it is likely that AI voices will become increasingly similar to human voices in the future.

How are AI Voices created?

AI voices are created using a combination of machine learning techniques, data processing, and digital signal processing. The process typically involves the following steps:

Data collection

To create an AI voice, large amounts of audio data featuring human voices are collected. This data includes various speakers, languages, accents, and speaking styles, ensuring diversity and richness in the training dataset.

Data preprocessing

The collected audio data is processed and cleaned to remove any noise or irrelevant information. The audio is then segmented into smaller chunks, often phonemes or words, and aligned with corresponding transcriptions.

Feature extraction

Features from the preprocessed audio data, such as pitch, timbre, and spectral characteristics, are extracted to represent the essential qualities of the human voice.

Model training

A machine learning model, usually a deep neural network, is trained using the processed audio data and extracted features. The model learns to generate speech by recognizing patterns and relationships within the dataset. Some popular models for AI voice generation include WaveNet, Tacotron, and FastSpeech.

Text-to-Speech (TTS) synthesis

Once the model has been trained, it can be used to convert text input into synthesized speech. The TTS system typically consists of two main components:

a. Text analysis: This component converts the input text into a sequence of phonemes or linguistic features, which include information about prosody (intonation, stress, and rhythm).
b. Audio synthesis: This component uses the trained neural network to generate audio waveforms from the linguistic features or phoneme sequences. The synthesized audio is then fine-tuned and adjusted for naturalness and intelligibility.

Post-processing

The generated audio may be further processed to enhance its quality, such as adding reverberation, equalization, or other audio effects.

Evaluation and refinement

The AI-generated voice is evaluated for naturalness, intelligibility, and expressiveness using objective measures, subjective listening tests, or both. Based on the evaluation, the AI model and TTS system may be refined and retrained to improve the voice quality.

As AI and machine learning techniques continue to advance, the process of creating AI voices is becoming more efficient, and the resulting voices are becoming more natural and expressive.

What will it take to make AI voices more like natural voices?

To make AI voices more like natural voices, several aspects need to be addressed and improved. Here are some key factors to consider:

High-quality training data

To create more natural-sounding AI voices, large and diverse datasets featuring various languages, accents, emotions, and speaking styles are required. The quality of the data plays a significant role in shaping the performance of the AI voice.

Improved modeling techniques 

Developing and refining machine learning models, such as deep neural networks, that can better capture the nuances and complexities of human speech will be essential. Techniques like WaveNet, Tacotron, and FastSpeech have shown great promise, but there is still room for improvement.

Expressiveness and emotion

 AI voices should be able to convey a wide range of emotions, as well as the subtleties and variations in pitch, tone, and intensity that are characteristic of natural human speech. This can be achieved by incorporating emotional context and prosody information into the training data and the AI models.

Context-awareness

Natural human speech is highly context-dependent, adapting to different situations, social settings, and conversation partners. Developing AI models that can understand and adapt to context will make the voices more natural and versatile.

Handling imperfections

Human speech contains imperfections like stutters, hesitations, and breath sounds that contribute to its naturalness. Incorporating these imperfections into AI-generated voices, without compromising intelligibility, can make them sound more human-like.

Personalization

Human voices are unique and exhibit individual variations. To make AI voices more natural, they should be able to mimic or generate personalized speaking styles, vocal characteristics, and idiosyncrasies.

Creativity and adaptability

Natural speech includes creative expressions, such as metaphors, analogies, and humor, which can be challenging for AI-generated voices to replicate. Developing AI models that can understand and generate creative language while adapting to various linguistic contexts will make the voices more natural and engaging.

Continuous learning and evaluation

AI models should be able to learn from new data and user feedback to continually refine their performance. Regular evaluation of the generated voices using objective measures and subjective listening tests will help identify areas for improvement and guide further research and development.

Addressing these factors and incorporating advancements in AI research, machine learning, and digital signal processing will help make AI voices more like natural human voices over time.

Vocal Imperfections That Make Speech Sound Natural

Of particular note, however, are the imperfections of the human voice.  As regular, everyday people, we don’t notice the ums and aahs that come with everyday speech.  When a computerized voice lacks pauses, breaths or other vocal idiosyncrasies, we find it unnerving. 

For AI voices to become more human if you will, they’ll either need to replicate these nuances, or we will adapt to listening to overly perfect sounding speech.

Human speech imperfections can be described as the natural inconsistencies and irregularities in speech patterns that make human voices unique and authentic. These imperfections contribute to the overall character and expressiveness of speech. Some tangible examples of human speech imperfections include:

Disfluencies

Disfluencies are interruptions or irregularities in the flow of speech. Examples of disfluencies include hesitations, filled pauses (using sounds like “uh” or “um”), and repetitions (repeating words or phrases). Disfluencies can occur when a speaker is searching for the right word, experiencing uncertainty, or dealing with cognitive overload.

Stuttering

Stuttering is a speech disorder characterized by involuntary disruptions in the normal flow of speech. It can manifest as repetitions of sounds, syllables, or words; prolongations of sounds; or blocks where the speaker is unable to produce any sound for a brief period.

Accents and dialects

Accents and dialects are variations in speech patterns that arise from geographical, social, or cultural factors. They can include differences in pronunciation, vocabulary, and grammar. These variations contribute to the richness and diversity of human speech.

Articulation errors

Articulation errors occur when a speaker has difficulty pronouncing certain sounds or sound combinations correctly. These errors can include substitutions (replacing one sound with another), omissions (leaving out a sound), or distortions (producing a sound incorrectly).

Breathing sounds

Breathing sounds, such as inhalations and exhalations, are a natural part of human speech. These sounds can be more prominent in some speakers and may vary depending on the speaker’s health, emotions, or speaking style.

Voice irregularities

Human voices can exhibit irregularities in pitch, volume, and timbre due to factors like vocal fold vibrations, resonance, and airflow. These irregularities can make a voice sound more expressive, emotional, or unique.

Laughter, sighs, and vocalizations

Human speech often includes non-verbal vocalizations, such as laughter, sighs, or other expressive sounds that convey emotions, attitudes, or reactions to a particular situation.

Infrequent speech errors

Sometimes, people may mispronounce words, swap sounds in words, or use incorrect grammar. These errors can happen due to slips of the tongue, lapses in attention, or language processing mistakes.

Incorporating these imperfections into AI-generated voices can help make them sound more natural and human-like. However, it’s important to strike a balance between incorporating imperfections and maintaining the intelligibility and clarity of the generated speech.

Until then, hopefully we can all detect the differences between AI generated speech and that created naturally by our fellow human beings.

Wanting to learn more about AI Voice? Read our blog about the top AI Voice Conferences in 2023.