|Exploring TTS Technology: An Essential Glossary of Terms||An overview of important terms and concepts related to Text-to-Speech (TTS) technology.|
|Unveiling Advantages of Implementing iOS Text to Speech API||An exploration of the benefits and advantages of utilizing the iOS Text to Speech API.|
|Practical Use Cases: Harnessing the Power of iOS Text to Speech API||Real-world examples and scenarios showcasing the practical applications of the iOS Text to Speech API.|
|Recent R&D Innovations in Text to Speech Technology||An overview of the latest research and development advancements in the field of Text to Speech technology.|
Exploring TTS Technology: An Essential Glossary of Terms
Speech Synthesis: A technology that converts written text into spoken words—often used in applications such as text-to-speech, voice-enabled services, and other language-based user interfaces.
Utterance: In the realm of speech synthesis, an utterance refers to a piece of text that is to be synthesized into speech. It is represented by the SpeechSynthesisUtterance interface in the Web Speech API.
W3C (World Wide Web Consortium): An international community that develops open standards to ensure the long-term growth of the Web. They are responsible for the development and maintenance of the Web Speech API.
Pitch: In speech synthesis, pitch refers to the perceived frequency of the sound produced. It can be adjusted to make the synthesized speech sound higher or lower.
Rate: The speed at which the synthesized speech is read out. It can be adjusted to make the speech faster or slower.
Volume: The loudness of the synthesized speech. It can be adjusted using the Web Speech API.
onstart and onend Events: These are event handlers in the Web Speech API that are triggered at the start and end of the speech synthesis respectively. They can be used to perform certain actions when the speech starts or ends.
Unveiling Advantages of Implementing iOS Text to Speech API
Unveiling the iOS Text to Speech API reveals a plethora of technical features, each with its own distinct advantages and benefits. At its core, this API allows for the conversion of text into speech—a feature that is not only advantageous for developers seeking to enhance the auditory experience of their applications, but also beneficial for users with visual impairments or literacy challenges. Its compatibility with the iOS platform ensures seamless integration, while its flexibility in voice selection, pitch, and volume control offers a customizable user experience. Furthermore, its ability to function offline provides an added layer of convenience, making it an invaluable tool in the realm of mobile application development.
Practical Use Cases: Harnessing the Power of iOS Text to Speech API
As businesses become increasingly aware of the potential of iOS Text to Speech API, they encounter a common problem—how to effectively leverage this technology for practical applications. This API, with its advanced speech synthesis capabilities, offers a solution. It allows developers to create applications that can convert text into human-like speech, enhancing user experience and accessibility. For instance, ecommerce platforms can implement this API to read product descriptions aloud, improving customer engagement and potentially boosting sales. Furthermore, enterprise-level organizations can use it to develop assistive technologies for visually impaired employees, promoting inclusivity in the workplace. Thus, the iOS Text to Speech API positions businesses at the forefront of technological innovation, providing them with a competitive edge in today's digital landscape.
Recent R&D Innovations in Text to Speech Technology
Unveiling cutting-edge research in TTS synthesis—business, education, and social applications reap immense benefits. Knowledge of recent engineering case studies sparks interest, fuels desire for innovation, and prompts action towards adopting this transformative technology.
- Authors: Archana Balyan, S. S. Agrawal, Amita Dev
- Download URL: https://www.ijert.org/research/speech-synthesis-a-review-IJERTV2IS60087.pdf
- Subjects: Text-to-Speech synthesis, Machine Learning, Deep Learning
- Summary: This research paper reviews recent research advances in R&D of speech synthesis with focus on one of the key approaches i.e. statistical parametric approach to speech synthesis based on HMM, so as to provide a technological perspective. In this approach, spectrum, excitation, and duration of speech are simultaneously modeled by context-dependent HMMs, and speech waveforms are generated from the HMMs themselves. This paper aims to give an overview of what has been done in this field, summarize and compare the characteristics of various synthesis techniques used. It is expected that this study shall be a contribution in the field of speech synthesis and enable identification of research topic and applications which are at the forefront of this exciting and challenging field.
- Author: Sevinj Yolchuyeva
- Download URL: https://www.researchgate.net/publication/353393158_Novel_NLP_Methods_for_Improved_Text-To-Speech_Synthesis
- Date of Publication: June 2021
- Subjects: Deep Learning, Machine Learning, Natural Language Processing (NLP), neural Text-To-Speech
- Summary: The goal of this dissertation is to introduce novel NLP methods, which have a relation directly or indirectly to serve in improving TTS synthesis. These methods are also useful for automatic speech recognition (ASR) and dialogue systems. In this dissertation, covered are three different tasks: Grapheme-to-phoneme Conversion (G2P), Text Normalization and Intent Detection. These tasks are important for any TTS system explicitly or implicitly. As the first approach, convolutional neural networks (CNN) is investigated for G2P conversion. Proposed is a novel CNN-based sequence-to-sequence (seq2seq) architecture. This approach includes an end-to-end CNN G2P conversion with residual connections, furthermore, a model, which utilizes a convolutional neural network (with and without residual connections) as encoder and Bi-LSTM as a decoder. As the second approach, the application of the transformer architecture is investigated for G2P conversion and compared its performance with recurrent and convolutional neural network-based state-of-the-art approaches. Beside TTS systems, G2P conversion has also been widely adopted for other systems, such as computer-assisted language learning, automatic speech recognition, speech-to-speech machine translation systems, spoken term detection, spoken document retrieval. When using a standard TTS system to read messages, many problems arise due to phenomena in messages, e.g., usage of abbreviations, emoticons, informal capitalization and punctuation. These problems also exist in other domains, such as blogs, forums, social network websites, chat rooms, message boards, and communication between players in online video game chat systems. Normalization of the text addresses this challenge. Developed is a novel CNN-based model, and this model is evaluated on an open dataset. The performance of CNNs is compared with a variety of different Long Short-Term Memory (LSTM) and bi-directional LSTM (Bi-LSTM) architectures on the same dataset. Intent detection forms an integral component of such dialogue systems. For intent detection, develop is a novel models, which utilize end-to-end CNN architecture with residual connections and the combination of Bi-LSTM and Self-attention Network (SAN). These are also evaluated on various datasets.
- Authors: Fahima Khanam, Farha Akhter Munmun, Nadia Afrin Ritu, Muhammad Firoz Mridha, Aloke Kumar Saha
- Download URL: http://www.jait.us/uploadfile/2022/0831/20220831054604906.pdf
- Date of Publication: August 31, 2022
- Subject: Business and Technology
- Summary: In this research paper, a taxonomy is introduced which represents some of the Deep Learning-based architectures and models popularly used in speech synthesis. Different datasets that are used in TTS have also been discussed. Further, for evaluating the quality of the synthesized speech, some of the widely used evaluation matrices are described. Finally, the research paper concludes with the challenges and future directions of the TTS synthesis system.
Unreal Speech, a revolutionary TTS platform, is making waves in the industry with its cost-effective solutions. It has been proven to slash TTS costs by up to 95%, making it up to 20 times cheaper than competitors like Eleven Labs and Play.ht, and up to 4 times cheaper than tech giants such as Amazon, Microsoft, IBM, and Google. This cost efficiency is not at the expense of quality—Unreal Speech features a studio-quality voice over tool, Unreal Speech Studio, for creating professional podcasts, videos, and more. Users can also experience the technology firsthand through a simple, live web demo—Unreal Speech demo—where they can generate random text and listen to the human-like voices of Unreal Speech.
Not only does Unreal Speech offer a wide variety of professional-sounding, human-like voices, but it also allows users to customize playback speed and pitch to generate the desired intonation and style. The pricing structure of Unreal Speech is designed to scale with the needs of various businesses and organizations, from small to medium businesses, call centers, and telesales agencies, to podcast and audio book authors, content publishers, video marketers, and more. The pricing tiers range from a free tier offering 1 million characters or around 22 hours of audio, to an enterprise tier supporting up to 3 billion characters per month at discounted rates. This flexibility in pricing, coupled with the high-quality output and 99.9% uptime guarantee, has led to high praise from users, such as Derek Pankaew, CEO of Listening.io, who stated, "Unreal Speech saved us 75% on our TTS cost. It sounds better than Amazon Polly, and is much cheaper."
Is Google text to speech API free?
Google's TTS API is not offered free of charge—it operates on a pay-as-you-go pricing model. The cost is determined by the volume of characters processed by the API, with a specific rate applied per million characters. It's crucial to note that the API's usage is not limited to the English language; it supports a multitude of languages and dialects, providing businesses with a versatile tool for global communication. Furthermore, the API supports SSML, allowing developers to fine-tune the speech output for a more natural and engaging user experience.
Businesses and companies can leverage Experimenting With speechSynthesis, a resource dated February 14, 2017. This page offers insights into practical applications of speech synthesis, fostering innovation and competitive advantage in the digital marketplace.
Educational institutions, healthcare facilities, government offices, and social organizations can benefit from Web Speech Synthesis Demo. This basic demonstration of web speech synthesis supports learning, accessibility, and communication efforts across various sectors.