HarmonyOS Next Speech Synthesis: An In-Depth Analysis

This article delves into the speech synthesis technology within Huawei's HarmonyOS Next (API 12), offering a practical developer's perspective. We'll explore core principles, functional requirements, implementation details using the Core Speech Kit, and strategies for optimization and application expansion.

I. Principles and Functional Requirements of Speech Synthesis

(1) Basic Principles

HarmonyOS Next's speech synthesis magically transforms text into spoken words. This involves two key steps: text analysis and speech synthesis modeling.

Text Analysis: The system preprocesses input text, performing tasks like word segmentation, part-of-speech tagging, and prosody analysis. For instance, the sentence “今天天气真好。” (The weather is really nice today.) is segmented into words (“今天,” “天气,” “真好”), each tagged with its part of speech. Prosodic analysis identifies stressed words and intonation patterns, providing crucial information for the next stage.

Speech Synthesis Modeling: Common models include parameter-based synthesis and waveform concatenation. Parameter-based methods generate speech parameters (fundamental frequency, formants) from the text analysis results via an acoustic model. A vocoder then converts these parameters into a speech waveform. Waveform concatenation selects and joins pre-recorded speech segments from a library based on text analysis, producing a more natural result.

(2) Analysis of Functional Requirements

1. Multilingual Support: HarmonyOS Next needs to support diverse languages. The inherent differences in grammar, pronunciation, and prosody between languages (e.g., tonal languages like Chinese vs. intonation languages like English) necessitate language-specific models and pronunciation libraries.
2. Speech Style Customization: Users require diverse speech styles. A friendly style for smart assistants differs from the emotive style needed for audiobooks. The technology must offer customizable styles to meet varied application needs.

(3) Comparison of Different Speech Synthesis Technologies

1. Parameter-based vs. Waveform Concatenation: Parameter-based synthesis offers better control over timbre and prosody and requires less storage. However, its synthesized speech is less natural, especially with complex linguistic phenomena. Waveform concatenation generates highly natural speech but demands vast storage and higher computational resources.
2. Vendor Comparison: Different vendors provide speech synthesis technologies with varying strengths in specific languages or scenarios. Choosing the right technology depends on the application’s needs and target audience.

II. Implementation of Speech Synthesis Function in Core Speech Kit

(1) Introduction to Functional Interfaces and Classes

The Core Speech Kit provides essential interfaces and classes for integrating speech synthesis into HarmonyOS Next applications. The TextToSpeechEngine class is central, offering methods to create an engine, set parameters, and synthesize speech.

(2) Code Example and Speech Parameter Settings

import { textToSpeech } from '@kit.CoreSpeechKit';

// Create a speech synthesis engine
let ttsEngine = textToSpeech.TextToSpeechEngine.create();

// Set speech parameters
ttsEngine.setPitch(1.2); // Set pitch (1.0 is normal)
ttsEngine.setSpeed(0.8); // Set speed (1.0 is normal)

// Text to synthesize
let text = "欢迎使用HarmonyOS Next语音合成技术。"; // Welcome to use HarmonyOS Next speech synthesis technology.

// Synthesize speech
ttsEngine.speak(text);

(3) Evaluation of the Naturalness and Smoothness of the Synthesized Speech

The Core Speech Kit generally provides natural and smooth speech for common text. Pronunciation is accurate, intonation is natural, and semantics are well-expressed. However, challenges may arise with rare characters, technical terms, or complex sentence structures. Overall, the quality meets the demands of most everyday applications.

III. Application Expansion and Optimization of Speech Synthesis

(1) Expansion of Application Scenarios

1. Smart Assistants: Speech synthesis is crucial for natural human-computer interaction. Smart assistants need to respond with clear, natural voices to user queries, such as weather information: “今天天气晴朗，气温25摄氏度，适合外出活动。” (Today is sunny, 25 degrees Celsius, ideal for outdoor activities).
2. Audiobooks: Speech synthesis converts text into compelling audio readings. Optimizing parameters (timbre, speed, intonation) for different characters and plot points enhances immersion.

(2) Optimization Strategies

1. Data Augmentation: Techniques like pitch shifting, speed alteration, and adding noise to training data improve the model's robustness and naturalness. Collecting diverse speech data further enhances synthesis quality.
2. Model Optimization: Employ lightweight neural network architectures and compression techniques (pruning, quantization) to reduce model size and resource consumption while maintaining performance.

(3) Development Experience and Precautions

1. Text Preprocessing: Ensure correct text formatting and encoding, handle special symbols and abbreviations appropriately (e.g., convert “&” to “和,” and “etc.” to “等等”).
2. Speech Parameter Tuning: Adjust parameters carefully to avoid unnatural speech. Balance speed and clarity. Monitor device performance and user feedback to refine settings.