|Comprehensive Glossary: Understanding Key Terms in TTS Tech||Key terms and definitions related to text-to-speech (TTS) technology.|
|Latest Research & Development Innovations in TTS Technology||Recent advancements and innovations in text-to-speech technology.|
Comprehensive Glossary: Understanding Key Terms in TTS Tech
Speech Synthesis: Speech Synthesis is the artificial production of human speech, often used in conjunction with text-to-speech technology. It allows a system to generate spoken language based on written input.
Web Speech API: The Web Speech API is a web-based API for voice data conversion. It includes two components: SpeechSynthesis (Text-to-Speech), and SpeechRecognition (Asynchronous Speech Recognition).
SpeechRecognition: SpeechRecognition is a part of the Web Speech API that enables the recognition and translation of spoken language into written text.
Utterance: In the context of the Speech Synthesis API, an utterance refers to a piece of text that is to be synthesized into speech.
Voices: Voices, in the context of text-to-speech, refer to the different types of synthesized voices that can be used to read out the text. These can vary in accent, pitch, speed, and language.
Asynchronous: Asynchronous refers to operations that do not block other operations from executing until they have finished. In the context of the Web Speech API, it means that speech recognition or synthesis can occur while other operations continue.
Latest Research & Development Innovations in TTS Technology
Grasping the latest research in TTS synthesis—coupled with recent engineering case studies—offers a plethora of advantages. It piques interest by providing insights into cutting-edge technology, fuels desire by showcasing potential applications in business, education, and social sectors, and prompts action by demonstrating tangible benefits. This knowledge empowers businesses to enhance customer experience, educators to create inclusive learning environments, and social platforms to foster accessibility.
- Download URL: https://doi.org/10.48550/arXiv.2106.15561
- Date of publication: June 29, 2021
- Authors: Xu Tan, Tao Qin, Frank Soong, and Tie-Yan Liu of Cornell University's Electrical Engineering and Systems Science department
- Subject: Audio and Speech Processing
- Summary: This paper provides a comprehensive survey on neural TTS synthesis, covering key components such as text analysis, acoustic models, and vocoders. It also explores advanced topics including fast TTS, low-resource TTS, robust TTS, expressive TTS, and adaptive TTS. The paper summarizes relevant resources and discusses future research directions, making it valuable for both academic researchers and industry practitioners in the field of TTS.
- Download URL: https://web.stanford.edu/class/cs224s/project/reports_2017/Yuan_Li.pdf
- Date of publication: 2017
- Authors: Yuan Li, Xiaoshi Wang, and Shutong Zhang of Stanford University's Department of Computer Science
- Subjects: Deep Learning, Machine Learning, Text-to-Speech synthesis
- Summary: This research project focuses on building a parametric TTS system based on WaveNet, a deep neural network introduced by DeepMind. The model utilizes convolutional layers to extract valuable information from input data and generate raw audio waveforms. The paper discusses the model's performance and identifies areas for improvement, providing insights into the challenges of TTS synthesis.
- Download URL: https://doi.org/10.48550/arXiv.2205.04421
- Date of publication: May 9, 2022
- Authors: Xu Tan, Jiawei Chen, Haohe Liu, Jian Cong, Chen Zhang, Yanqing Liu, Xi Wang, Yichong Leng, Yuanhao Yi, Lei He, Frank Soong, Tao Qin, Sheng Zhao, and Tie-Yan Liu of Cornell University's Electrical Engineering and Systems Science department
- Subject: Audio and Speech Processing
- Summary: This paper defines human-level quality in TTS synthesis and presents NaturalSpeech, an end-to-end TTS system that achieves human-level quality on a benchmark dataset. The system utilizes a variational autoencoder (VAE) for text-to-waveform generation, incorporating modules such as phoneme pre-training, differentiable duration modeling, bidirectional prior/posterior modeling, and a memory mechanism in VAE. Experimental evaluations demonstrate the system's performance, showing no statistically significant difference from human recordings at the sentence level.
# Import the required library import pyttsx3 # Initialize the Speech Engine engine = pyttsx3.init() # Set the text you want to convert to speech text = "Hello, this is a quick Python example of Text to Speech." # Use the say() method to convert TTS engine.say(text) # Run the speech engine engine.runAndWait()
This Python example demonstrates a simple implementation of a TTS conversion. The pyttsx3 library is used, which is a TTS conversion library in Python. The text to be converted is set and the say() method is used to initiate the conversion. Finally, the runAndWait() method is called to execute the speech engine.
Unreal Speech emerges as a game-changer in the realm of TTS technology, offering a cost-effective solution that outperforms its competitors. It significantly reduces TTS costs by up to 95%, making it up to 20 times cheaper than Eleven Labs and Play.ht, and up to 4 times cheaper than tech giants like Amazon, Microsoft, IBM, and Google. This cost efficiency is a boon for a wide array of sectors, including small to medium businesses, call centers, telesales agencies, content publishers, game developers, healthcare facilities, financial agencies, educational institutions, and more. The pricing structure of Unreal Speech is designed to scale with the needs of these diverse organizations, offering volume discounts and custom solutions for high-volume clients.
But cost efficiency is not the only advantage Unreal Speech brings to the table. It also offers the Unreal Speech Studio, a tool that enables users to create studio-quality voice overs for podcasts, videos, and more. Users can customize playback speed and pitch to generate the desired intonation and style, and choose from a wide variety of professional-sounding, human-like voices. Furthermore, users can try out the technology with a simple to use live Unreal Speech demo for generating random text and listening to the human-like voices of Unreal Speech. The audio output can be downloaded in MP3 or PCM µ-law-encoded WAV formats in various bitrate quality settings.
Unreal Speech's commitment to quality and performance is evident in its customer testimonials. Derek Pankaew, CEO of Listening.io, attests to the superior quality and cost efficiency of Unreal Speech, stating that it saved his company 75% on TTS costs while delivering a high-quality listening experience. Unreal Speech's robust infrastructure supports up to 3 billion characters per month for each client, with 0.3s latency and 99.9% uptime guarantees. This level of performance, combined with its cost efficiency and quality, makes Unreal Speech a compelling choice for organizations seeking a reliable, high-quality TTS solution.
Is Google text to speech API free?
Google's TTS API is not entirely free—it operates on a pay-as-you-go model. The first million characters processed in a month are free, but subsequent usage incurs a cost. The pricing varies based on the type of voice selected (standard or WaveNet), and the region of usage. It's crucial for developers to monitor their usage to avoid unexpected charges. The API, part of Google Cloud's suite of machine learning tools, supports multiple languages and voices, and allows customization via SSML tags.
What is the Web API for text to speech?
The Web API for TTS, such as MS's Azure Cognitive Services, provides a robust SDK for developers to integrate TTS functionality into their applications. It leverages advanced neural network algorithms to synthesize natural-sounding human speech. The API supports a wide range of languages and voices, and allows for detailed customization using SSML. Developers can control aspects like pronunciation, volume, pitch, and speed of the speech output. The API is designed to be easy to use, with comprehensive documentation and sample code available to assist in the integration process.