Text To Speech (TTS) is part of the human-machine dialogue, allowing the machine to speak. It is an outstanding work that uses both linguistics and psychology. With the support of the built-in chip, it intelligently transforms text into a natural voice stream through the design of a neural network.
Text To Speech (TTS) technology converts text in real-time, and the conversion time can be calculated in seconds. Under the action of its unique intelligent voice controller, the voice rhythm of text output is smooth. This makes the listener feel natural when listening to information, without the indifference and jerky feeling of machine voice.
Text To Speech (TTS) is a type of speech synthesis application that converts files stored in the computer into natural speech output, such as help files or web pages. Text To Speech not only helps visually impaired people read the information on the computer but also increases the readability of text documents. Text To Speech applications include voice-driven mail and voice-sensitive systems and are often used with voice recognition programs.
Text To Speech (TTS) is generally divided into two steps:
What this step does is to convert the text into phoneme sequence, and mark the start and end time, frequency change and other information of each phoneme.
As a preprocessing step, its importance is often overlooked, but it involves many issues worthy of research, such as the distinction of words with the same spelling but different pronunciations, the processing of abbreviations, and the determination of pause positions, etc.
In a narrow sense, this step specifically refers to generating speech based on phoneme sequences (and marked start and end times, frequency changes, etc.). In a broad sense, it can also include text processing steps.
There are three main types of methods in this step:
We can divide a speech synthesis system into a splicing synthesis system and a parameter synthesis system. When we introduce the neural network into the parameter synthesis system as a model, the synthesis quality and naturalness of the parameter synthesis system get significantly improved. On the other hand, the popularity of IoT devices (such as smart loudspeaker boxes and smart TVs) also imposes computing resource constraints and real-time rate requirements for the parameter synthesis systems deployed on the devices. The Deep Feedforward Sequential Memory Network (DFSMN) we have introduced in this study can maintain the synthesis quality, while effectively reducing the computational usage, and improving the synthesis speed.
NLP refers to the evolving set of computer and AI-based technologies that allow computers to learn, understand, and produce content in human languages. The technology works closely with speech/voice recognition and text recognition engines. While text/character recognition and speech/voice recognition allows computers to input the information, NLP allows making sense of this information.
Intelligent Speech Interaction is suitable for various scenarios, including intelligent Q&A, intelligent quality inspection, real-time subtitling for speeches, and transcription of audio recordings. Intelligent Speech Interaction has been successfully applied in many industries such as finance, insurance, e-commerce and smart home. Intelligent Speech Interaction allows you to use self-learning platform to improve speech recognition accuracy and provides a comprehensive management console and easy-to-use SDKs. You are welcome to activate Intelligent Speech Interaction.
This Artificial Intelligence Service solution empowers you to build various types of multi-language customer service chatbots to enable text, voice, and image interactions. With pre-trained, artificial intelligence algorithms, you can set up a knowledge base to provide a consistent and engaging user experience for sales, support, and upsells. After sufficient training, your customer service system would become smarter and more intelligent. Additionally, this solution provides you with smart operations and management of customer service centers, including volume prediction, routing, manpower planning, and real-time dispatching depending on productivity and quality priorities.
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