ai voice text to speech

ai voice text to speech

The Power of AI Voice Text to Speech

1. Introduction

For centuries, humans have had the capability to read text aloud contained in books, newspapers, etc. to improve learning or communication between other people. This activity was done by the option of narrative reading, a profession such as librarian/priest, etc., or just reading it by yourself. This effort to read text aloud consumed a lot of time and money because this activity was done manually. With the innovation of technology in the 20th century, a text-to-speech device was invented under the work of several scientists. This device can transform text into speech and it can be plugged into a computer or other devices. This innovation was a breakthrough, but it still has drawbacks because the voice produced by this device was monotonous and sometimes hard to understand by listeners. As a result, the development of the text-to-speech device was not very significant because many people still prefer the old-fashioned way to read text aloud.

2. Benefits of AI Voice Text to Speech

There are numerous benefits to using AI voice text to speech. First and foremost, it has been a giant leap forward in accessibility technology for the visually impaired. It allows those who cannot read from standard text the opportunity to listen to written words. In combination with the internet and services that have been created to input voice data, it effectively allows a visually impaired person to have a virtual assistant read them information from any source on the internet. The cost of employing someone to read information has been quite costly in the past, and the use of TTS can save the visually impaired money, allowing them to potentially afford more services. Creating a system for a handheld device that uses GPS location, they could be automatically informed whenever there was an emergency in their area. For example, if there was a chemical spill near the home of a person with respiratory issues, their potential assistant could inform them of the situation, even if they were not aware of the news.

3. Applications of AI Voice Text to Speech

Information today is being digitized. Book readers are one such example that takes traditional printed text and converts it into an electronic form. Kindle is one of the most successful book readers. Users are able to purchase books online where Kindle has a feature to read the book’s text. This is achieved through TTS systems. E-learning is becoming increasingly popular due to its accessibility of studying from anywhere. There are various tools that convert e-learning content into audio format. This can benefit blind and vision-impaired people. They can use audio screen readers where TTS system is integrated to read the screen content. This will allow a more interactive educational experience. Also, foreign language students who are learning the language may find it helpful to understand the pronunciation and accent of the language by using a TTS system for language conversion.

Today, companies need to operate 24/7 and hence they need to automate their services in order to decrease the operating cost. This resulted in greater demand for TTS systems. Companies belonging to various domains such as call center, banking, and automotive are in need of TTS systems. For instance, a leading insurance company in the UK who previously outsourced data entry operators to read their clients’ resumes had tried to automate this by creating a tool that uses machine learning techniques to read the resume and fill the form using the information read. This tool was successful in terms of cost reduction and was well received by its users. This shows that adopting TTS systems can greatly benefit such companies. AI systems are being used in gaming applications to provide an enhanced and sophisticated game environment. One such application is “Neverwinter Nights,” a premium game which uses TTS systems by integrating Microsoft’s SAPI SDK. This game provides a new experience by allowing the game characters to read out conversations rather than the user having to read the text.

4. Challenges and Limitations

The limiting factor is the intelligibility of the synthesized speech and the voice recognition capability of the speech system. These two issues are usually interrelated. The more sophisticated the chosen voice, the less intelligible the synthesis. Intelligibility can also be compromised if a voice is improperly configured in the synthesis engine. Voice recognition rates tend to be lower for more intelligible but less natural-sounding synthetic voices. It has been found that the recognition of synthetic speech is around 10-15% lower than the recognition of natural speech in the same controlled study. High quality synthetic speech has been found to be around 85% intelligible to a general listener, but the twenty percent loss in intelligibility from perfect intelligibility is significant, making truly human-like TTS an open research problem. This is a fundamental weakness in testing modern synthesis systems, whereby a system that talks like a human may have listeners failing to realize that it is a TTS system. It is arguable whether an 85% intelligible human-like synthetic speech voice is competitive with a natural speech recording at the same level of intelligibility. Research in automatic speech recognition and TTS must fundamentally interact, as having good synthesis is wasted if there are no methods to input the desired text, and the goal in TTS is to obviate the need to type the text to be synthesized. Current recognizers are trained using natural speech data, and so tend to perform poorly when presented with synthetic speech. Adapting recognizers to work well with artificial speech is a significantly difficult task that has not been hugely successful. TTS developers also wish to make TTS systems that can easily talk with one another. Most TTS systems are today designed to read text aloud from a single style of input, but there is a growing need for TTS systems to be used as reading aids for the visually impaired, and such a system will need to be able to read the content of any speech synthesizer easily.

5. Conclusion

We have considered a variety of design dimensions involved in the development of believable character agents. We described how research in AI has led to the development of technologies that can automate the generation of agent behavior starting from abstract specifications and that can automatically learn new behavior by observing and imitating humans and by being trained through feedback. This work can provide the bridge between work in AI and work in interface design. It can provide interface designers with tools that embody their informal design knowledge and that can translate high-level design goals into concrete implementations. In summary, we believe the time is ripe to develop a new generation of interface agents. We hope this research will enable agents to interact more naturally and effectively with human users and will provide a powerful new means for automated knowledge acquisition about user tasks and preferences. Our articles provide a foundation for this research through detailing the various design issues and the existing technological support for building interface agents. We hope it will convince interface designers that agent design is a promising new direction for interface design and will help them understand how to bring this technology to bear on practical interface design problems.