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3 Useful Innovations in AI Text-to-Speech Technology

In McKinsey’s 2021 global survey on artificial intelligence (AI), the AI adoption rate is steadily increasing, with 56% of respondents signifying the use of AI capabilities in at least one business function. This is higher than the 50% adoption rate registered in 2020. Likewise, an increasing number of businesses (27% compared to 22% in 2020) also attribute at least 5% of their earnings before interest and taxes (EBIT) to AI.

Text-to-Speech Technology

Indeed, AI is now seeing expansive utilization, particularly in service operations optimization. One of the ways enterprises are using AI to optimize their service delivery is through AI text-to-speech technology.

What Is AI Text-to-Speech Technology?

The term text to speech means exactly what you think it does. This technology takes written words and recites them.

This text reader voice technology is considered revolutionary for the purpose of improving accessibility to any document or information.That said, the earliest iterations of it left much to be desired.

Think Microsoft’s built-in text-to-speech voices. Yes, even the automated voices (e.g., Microsoft David, Microsoft Zira, and Microsoft Mark) that ship out with newer computer models still sound mechanical, stilted, and — particularly with their conspicuously unnatural inflections — artificial.

This is where AI text-to-speech trumps conventional text-to-speech. AI text-to-speech uses machine learning to “study” tens of thousands of human voices. In its immersion in databases of human voices, the computer program “learns” why humans raise and lower their voices, how they enunciate and accentuate their words, and what other vocal qualities humans use to convey their message and communicate.

In other words, AI text-to-speech utilizes the immense computational capabilities of computers to synthesize human-like voices. These new-generation, AI-driven text-to-speech synthesizers convert text to speech like traditional text-to-speech programs, but their output sounds much more dynamic and realistic. It’s as if an actual human, instead of a computer, is reading the text.

The quality of vocal generation in this AI age has become so advanced that it can be difficult, particularly in the case of short clips, to tell human and AI voices apart.

1. Multi Accent and Language

Have you ever heard people say dogs from other countries bark differently? In truth, dogs make the same sounds regardless of their country of origin. In other words, a dog from Germany will have no problem understanding a dog from Vietnam, and vice versa.

Humans, however, interpret dogs’ barks using their native language as a framework. This is why when a dog barks, a German will hear one thing, and a Vietnamese will hear something else.

What does this have to do with AI text-to-speech? It shows that people hear words differently depending on their language. A person’s language influences what he hears.

As such, if you wish to improve your audience’s understanding and comprehension of your spoken text, the best approach is to use a speaker whose vocal techniques are closest to that of your audience. Simply put, if you want your Japanese audience to understand better, use a Japanese speaker.

Barring that, you can use a seemingly Japanese AI speaker — an AI voice that approximates the accent and inflection of a native Japanese speaker. Even better, you can let the AI voice speak in your audience’s native language — i.e., the AI will read and speak Japanese.

Innovation: AI text-to-speech has evolved to the extent that AI can now speak in more than 60 non-English languages. Some AIs also provide accented English-speaking voices.

2. Multi-Style

How you talk varies depending on your audience. You probably don’t speak to your employer the way you talk to your wife, do you? Likewise, your speaking style also varies when you communicate with your colleagues, children, closest and dearest friends, and acquaintances.

Thus, there’s something positively unnatural about an AI voice reciting a love letter the same way it reads a financial analysis. You also probably expect a conversational tone instead of a newscaster-type voice when reading a dialog off a novel. Indeed, humans expect there to be speaking style variations depending on the audience and context.

Fortunately, AI voices are now available in a diverse range of styles. There are styles for delivering news and reports, and there are voices suited to more informal, casual dialogs. There are also style variations among digital bots and customer support AIs.

Innovation: AI voices come in various speaking styles, including newscast, conversational, customer service, chatbot, and in-car/smart assistant styles.

3. Emotional

The most significant difference between the AI voice and the human voice boils down to prosody or the patterns and rhythms of the speaking voice. They clue the listener in on the speaker’s attitude or affective state.

To illustrate, the words “Yeah, sure!” indicate agreement. However, spoken a particular way, they can mean the opposite.

Thus, for text-to-speech programs to become as good as human voice actors, they must provide AI voices that approximate the human voice’s speaking patterns and rhythm.

Fortunately, there are now AI voice generators with engines so advanced they can project human emotions. Their AI voices can be sad, solemn, and pensive. They can also be tongue-in-cheek and playful, and some can even laugh realistically.

Innovation: Advanced AI voice generation models make emotionally charged AI voices possible.

The Future of Text-to-Speech Generation Is Now

AI is one of the most significant technological developments of the modern age. It has wide-ranging applications in factories, sales, business analysis, and so much more. Even in human resources, AI is extremely useful in screening candidates and automated job market data analysis and processing.

Likewise, AI has always been the future of text-to-speech technology. This is evinced by multi-language, multi-accent, multi-style, and emotionally charged AI text-to-speech synthesizers that a casual listener will find challenging to distinguish from human readers.

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