6. Online Text-to-Speech (TTS)

1. Introduction

1.1 What is “TTS”?

TTS technology is a technology that converts written text into human-readable speech output. It enables computers to “read” text content and is widely used in various fields such as accessible reading, intelligent assistants, navigation systems, and educational software. Through TTS, users can hear natural and fluent human voices generated by machines, greatly improving the convenience and flexibility of information acquisition.

1.2 Brief Description of Implementation Principles

The implementation of a TTS system mainly includes the following key steps and technologies:

1. Text Analysis

• In this stage, the input text is first preprocessed, including but not limited to removing irrelevant characters, standardizing punctuation and capitalization, word segmentation, and recognizing special symbols such as numbers and converting them into their corresponding word forms.
• Simultaneously, linguistic analysis of the text is required, such as determining the pronunciation of each word (which usually requires the use of a pronunciation dictionary), stress positions, intonation patterns, and sentence structure.

2. Language Processing

• This step primarily focuses on how to correctly pronounce and adjust intonation based on context. For example, the word "read" has different pronunciations in different tenses (past tense/past participle is pronounced /red/, while in other cases it is pronounced /riːd/). Therefore, a powerful language model is needed to understand these subtle differences.
• This also involves prosodic modeling, i.e., determining which parts should be emphasized, whether the speech rate should be fast or slow, and the overall emotional tone of the sentence.

3. Speech Synthesis

• The text information processed in the previous two stages is fed into a speech synthesis engine, which is responsible for generating the actual sound waveforms.
• Traditional TTS systems use a concatenation synthesis method, which selects appropriate units from a pre-recorded speech segment database and concatenates them to form a complete sentence. While this method can produce high-quality speech, it is limited by the samples in the database.
• Modern TTS systems rely more on parametric synthesis or neural network synthesis (such as WaveNet, Tacotron, etc.). These methods can directly predict speech features from text and generate continuous sound signals. Especially deep learning-based methods can better capture subtle changes in speech, thus producing more natural and fluent sounds.

4. Sound Waveform Generation

• Finally, the generated sound waveform is further processed to ensure its quality meets expe