Text-to-speech models are aimed at learning the natural correspondence of text with the related phonetic and acoustic features, as well as with acoustic waveforms, using large datasets of human speech.
Modern TTS can mimic human speech in many languages in a knowledgeable and easily understandable manner.
AI advancements will slowly make TTS more natural sounding & open up various further use cases like voice assistants, audiobooks, accessibility, etc.
In this article, we will drill down into the details of TTS and weigh in with some thoughts on the future.
The history of text-to-speech (TTS) technology can be dated back to the age of the mechanization of the human voice in the 18th and 19th centuries. There were the first computer-based speech synthesis systems, for example, the vocoder created in 1961 by the employee of Bell Labs named John Larry Kelly Jr., in the 50s and 60s.
In the course of the 1970’s and the 1980s, TTS technology developed with the use of concatenative synthesis techniques and the development of new ways of producing and merging phonemes. In the 1990s pitch control and intonation were further developed.
Apple incorporated a TTS system to the iPhone in 2007 and incorporating TTS in the mobile devices in the 2000s stirred interest in TTS again. The TTS system was enhanced in the 2010s as a result of profound learning innovation and artificial intelligence. Screen readers, e-readers, and voice assistants are now widely using TS. As AI expands, TTS is only expected to become more popular.
What Is Text-To-Speech?
Text-to-speech (TTS) is a software that converts written text into spoken words. It interprets the text and produces a voice that imitates a human one with the help of correct linguistic rules and the functions of algorithms. The degree of sophistication of TTSs is a wide range, from mere robotic voices to the system mimicking human emotions and intonation.
It is applied in intelligent interaction systems, language teaching, and learning resources, and support systems for people with disabilities with low vision. Advanced speech deodorization is commonly executed by trendy TTS solutions with the help of deep learning algorithms that enhance the expressiveness of the given voice and representation quality.
Due to the possibility of integrating TTS into practically any device, starting from computers and ending with smart speakers and smartphones, it is flexible. It can enhance communication and information availability in various contexts.
Top AI Model for Text-to-Speech
Here are the top AI model for text-to-speech:
| Model Name | Description | Key Features | Use Cases |
| BASE TTS | A state-of-the-art TTS model by Amazon, trained on 100K hours of data. | 1 billion parameters, high naturalness, speaker ID disentanglement. | Voice assistants, audiobooks, gaming. |
| Deepgram Aura | Known for real-time conversations with minimal latency. | Less than 200ms latency, natural-sounding voices, conversational fillers. | IVR systems, AI voice agents, chatbots. |
| Microsoft Neural | Offers customizable TTS with natural-sounding outputs. | Deep neural networks for prosody prediction, high-fidelity audio. | Marketing, entertainment, vocal interfaces. |
| LOVO | Provides over 500 voices in 100 languages with emotional expressions. | Emotional modulation, diverse voice options. | Content creation, e-learning, marketing. |
| Google TTS | A widely used platform for TTS applications across various devices. | Supports multiple languages, customizable voices. | Mobile apps, accessibility tools. |
| Bark | Focuses on generating expressive and engaging speech. | High fidelity, expressive tone control. | Storytelling, video production, gaming. |
How Does Text-to-speech Work
A fascinating field of interest is found in the conversion of text to speech commonly referred to as – TTS- technology making texts breathe on their own. The following are the ways a text-to-speech works:
1. Examination of Texts
To start, the TTS system breaks down the written text into its most basic components: These are words, phrases and a collection of words that form a sentence. The academic dissection is significant to point out, as it starts the procedure of the further stages.
2. Language Interpretation
At this step, the system can comprehend the syntactic, punctuation, and formatting features of the text, as well as its intended irony or humor. This understanding allows the AI to create a conversational flow, which is close to what human beings talk about.
3. Artificial Voice
Here is where the real magic happens: If you thought the last couple of days’ outburst was quite vocal, then wait until you hear their voice synthesis. Natural voices or synthetic ones, which are either created by an AI or are pre-recorded, are used in TTS technology. These voices are also deliberately managed with a view to clarity and or realism. AI voices have continued to evolve and they now provide a much wider number of tones and accents used in the spoken output.
4. Speech Rendering
The last of them – speech rendering – deals with the organization of articulated speech elements, their intonation, and tempo. Here, the TTS system thoroughly decides the style of the vocalization of every word that is to be produced, the tone and the speed of pronunciation. This meticulous control of the mechanical aspect of the speech helps not only in getting accuracy but also in creating an exciting and easily palatable speech.
Image Source: Nvidia
Methods of Text-to-Speech Generation
The AI models hold different ways to perform text to speech conversion. Some of the methods for it are mentioned below:
Text-to-speech Concatenation
Concatenative TTS brings into use speech segments that are normally stored in CD ROM as phones, diphones, or syllables. These segments are joined to form complete utterances that make a natural-sounding speech, as has been stated above—however, the size of the database and the fact that it is constantly changing limits its effectiveness.
Parametric Text-to-speech
Employing mathematical models simulating the human voice-generation system, this technique generates speech. While parametric TTS is more flexible and needs less input compared to the concatenative approach, it generally does not possess the quality of the latter.
Text-to-speech Neural
Neural TTS is able to generate highly realistic and intelligible speech using deep learning models; WaveNet and Tacotron among them. This approach yields higher quality due to self-learning procedures involving the utilization of massive databases of recorded speech. However, when compared to other TTSs, it is more complex because of the many processing resources necessary for its functioning.
Definition with Example
There is an option in the text-to-speech conversion called the speech synthesis which can read aloud texts. For example, privately developed digital assistants like Alexa or Siri can be taken. The text “The weather today is sunny with a high of 75°F” gets converted into audible speech by the TTS system when you ask, “What’s the weather today?” A few processes are needed for this: voice analysis, vocabulary output, and choosing how to pronounce the texts that need to be generated.
For people with poor eyesight or visual impairment, textual content becomes a challenge; the text-to-speech features convert the content to be more readable and exciting. Therefore, TTS meets the need for the space between oral and written interaction, hence enhancing and or creating value in numerous application areas.
Step-by-step Process of Converting Text-to-speech
To convert text to voice (TTS), take the following methodical actions:
- Text to be Input: In the first place, get the text that you would like to convert to voice. There are two ways that you can achieve this, and that is, by importing it from a text file or by typing directly on the software program.
- Pick TTS Software: It is also appropriate to identify the particular TTS program or service that has to be chosen. Some of the most popular brands on the market include Amazon Polly, Microsoft Azure, and Google’s Text-to-Speech. Each of them may have different voices and voice activities that they may provide to the users.
- Text Analysis: It involves a checking of the input text by the TTS system. This means analyzing the text at the phoneme level and recognizing punctuation and the context in which the text is written to ensure correct pronunciation.
- Decide on Voice and Language: Choose the voice and language that you would prefer most. Some TTS systems offer a variety of voices or accents, even the possibility of selecting any of the emotional speaking modes.
- Text-to-Speech Conversion: Take action and begin the conversion. Considering the activities of text analysis, the output will be audio, in which the linguistic theory will be used to create natural-sounding speech.
- Preview and Edit: Here is the speech that has been created. Nearly all the platforms allow you to modulate the tone, tempo, or pronunciation to suit the situation at hand.
- Download Audio File: After you are satisfied with it, you can use the created audio file, which is usually in MP3 format, in any application.
- Utilize the Audio: For enhanced accessibility and interactivity, incorporate the finished audio in the presentation, movies, or as an assistive technology for visually impaired individuals.
Conclusion
By this we realized how Text-to-speech technology revolutionized the interfaces between man and machines as well as transformed the way that people consume information. It is also possible to achieve high levels of realism through ‘text to speech,’ perhaps with artificial intelligence at present. As technology advances, we may expect to see ever more realistic, perhaps more natural-sounding voices, the use of multiple languages, and the incorporation into an even wider choice of applications. TTS, for now, has an optimistic outlook, and its potential may bring about a complete revolution in S&T communication.
