Text-to-speech (TTS) is capable of transforming how businesses interact with their customers and partners, subject to its effective utilization. From making content accessible to all kinds of people and enhancing user experience, this technology can save time and money, bring automation into daily operations, and be scaled as per the requirements.
Let’s delve into the workings, importance, and implementation of Text-to-Speech (TTS) data solutions in the following sections.
Basics First | What is Text-to-Speech?
Text-to-speech (TTS) represents the evaluation and conversion of text to natural-sounding speech manifested into audio or video format. TTS was developed to help people with visual impairments and reading disabilities to listen to words and gain a better understanding.
However, with time, the technology evolved to cover a diverse range of use cases. So, it is easier to interact with customers over a call, empower virtual assistants, give instructions, etc.
In all this, the key element is the personification of a human accent in the speech, whether in audio or video format.
How Does Text-To-Speech Work?
The transformation process of written text into speech may seem effortless, but at the backend, you will observe a fascinating interplay of different techniques and technologies. Here’s how it works;
- Text Processing
- Text Normalization: The submitted text undergoes normalization to ensure consistent formatting across the data and remove irrelevant information.
- Tokenization: The normalized text then progresses to tokenization, where words are separated into individual units, followed by word segmentation into syllables or phonemes. These represent the basic building blocks of the speech.
- Part-of-Speech Tagging: Every word is assigned a relevant grammatical function, like noun, verb, etc., and understands the sentence structure and context.
- Text Analysis in TTS and Feature Extraction
- NLP Technique Implementation: Natural Language Processing analyzes the submitted text to interpret its meaning, intent, sentiment, and emphasis. This information is then evaluated to shape the speech’s intonation and delivery.
- Feature Extraction: Using the power of AI, the text’s pitch, volume, and speaking rate are determined. For this, the NLP process we use considers punctuation, sentence structuring, and the intended communication style.
- Speech Synthesis
- Select Acoustic Model: The first speech synthesis component worked upon is selecting an acoustic model to convert phonemes into actual sounds. For this, the technology we use identifies the various languages, accents, and speaking styles used.
- Generating Waveform: After TTS data collection and analysis, the chosen AI model will extract text features and linguistic information. It will then generate a sequence of digital audio samples to create a raw speech waveform.
Google launched Wavenet in 2016, which is a deep generative model of raw audio waveforms showing how Wavenets can generate speech mimicking human voice, texture, and sentiments.
Post-Processing
The waveforms generated undergo refinements to enhance naturalness and clarity. The most common techniques used here are smoothing and noise reduction.
The results are then checked and evaluated for quality assurance and sent for edits or customizations if required. In some cases of text analysis in TTS, voice customization is readily available. The users can change voice characteristics like pitch, speaking rate, and emotional tone.
Implementation of Applications of Text-to-Speech
Text-to-speech (TTS) is playing a proactive role in reshaping the conversational part of every process while offering tangible benefits to users and businesses.
- E-Learning
TTS is making e-learning accessible for individuals with learning disabilities, visual impairments, and limited language proficiency. They can listen and learn the content with the scope of personalizing the learning experiences. Moreover, based on effective TTS data collection, the learners can personalize the lessons and even gain knowledge in different languages.
- Customer Service
TTS-based chatbots can be available 24/7, answering customer queries and enhancing serviceability while reducing customer wait times. Moreover, chatbots provide personalized recommendations and guidance to create a human-like experience.
Effective TTS translation features can empower businesses to offer multilingual support, offering assistance to a wide range of customers and fostering inclusivity.
- Media Content Creation
Custom TTS solutions allows generation of high-quality and multilingual audio versions of written content, including books, research articles, articles, etc. News and media websites can convert written news content into audio format, superimposing it over an AI-based news anchor to share news with a wider audience.
- Accessibility and Assistive Technologies
TTS can empower individuals with visual impairments. Screen readers employ Text-to-Speech (TTS) data solutions to convert on-screen text into a legible and personalized audio speech format. This means people with visual impairments can use smartphones and computers.
Moreover, this technology can be used in real-time transcription captioning tools, delivering audio-to-text conversion. It can be used for audio-to-text conversion for meetings, lectures, and conferences.
While these are some major applications of the TTS technology, other use cases include GPS systems assistance in making public announcements in public places in different languages. In healthcare, doctors and nursing staff can leverage TTS to share medication instructions in the language of the patient’s choosing.
Ethical and Implementation Challenges in TTS
Within its undeniable benefits, TTS implementation and execution have some challenges along the way.
- Biased Data: TTS data collection and training is done with massive datasets of recorded speech and text. Biased information in any dataset pertaining to gender, ethnicity, and socioeconomic background can result in speech generation that mimics those biases. To avoid generating discriminatory, offensive, and misleading outputs, train the TTS model on authentic and unbiased data.
- Misinformation and Deepfakes: Anti-social elements can misuse TTS to create realistic-sounding computer voices to spread misinformation and run scams, impersonating a person. As this can have severe consequences for the victims, the end-users need to be informed about synthetic voices. Businesses and brands must share disclaimers about using TTS in their services and applications.
Conclusion
Text-to-speech (TTS) data solutions offer multiple advantages. But, their implementation require the provisioning of accurate and expansive data sets. At Shaip, we use expert-curated Text-to-Speech data sets, which can help you build advanced TTS solutions covering global languages.
Get in touch with us to learn more about our capabilities in TTS data solutions and services.
Author Profile: Hardik Parikh
With more than 15 years of experience creating and selling innovative tech products, Hardik is an accomplished expert in the field. His current focus is building and scaling Shaip’s AI data platform, which leverages human-in-the-loop solutions to provide top-quality training datasets for AI models.
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