Community Blog What is Text To Speech

What is Text To Speech

The article describes the Text To Speech definition and how it works.

Text To Speech Definition

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.

How Text To Speech Works

Text To Speech (TTS) is generally divided into two steps:

Text Processing

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.

Speech Synthesis

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:

  • Splicing method: Select the required basic units from a large number of recorded voices in advance to splice together. Such units can be syllables, phonemes, etc.; in order to pursue the coherence of synthesized speech, diphones (from the center of one phoneme to the center of the next phoneme) are often used as the unit. The speech quality synthesized by the splicing method is high, but it needs to record a large amount of speech to ensure the coverage.
  • Parametric method: According to the statistical model, the speech parameters (including the fundamental frequency, formant frequency, etc.) are generated at all times, and then these parameters are converted into waveforms. The parametric method also requires pre-recorded speech for training, but it does not require 100% coverage. The speech quality synthesized by the parametric method is worse than the splicing method.
  • Channel simulation method. The parameter used by the parameter method is the nature of the speech signal, and it does not pay attention to the process of speech generation. In contrast to this, the channel simulation is to establish a physical model of the channel and generate waveforms through this physical model. The theory of this method looks very beautiful, but because the voice generation process is too complicated, the practical value is not high.

Related Blog

Alibaba Research Introduces Deep Feedforward Sequential Memory Network (DFSMN) – A Novel Approach to Text-to-Speech (TTS) Systems

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.

Applications of NLP and Voice Recognition

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.

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