👨‍🎓Abstract

Abstract of the technical whitepaper of SpeechAI

Abstract

The advent of Artificial Intelligence (AI) and neural networks has revolutionized the field of Text-to-Speech (TTS) synthesis, transforming how machines generate human-like speech. This paper provides an overview of the principles underpinning AI and neural networks, with a focus on their application to TTS technologies. We delve into the architecture of neural networks, emphasizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been pivotal in advancing TTS systems. The relationship between AI, neural networks, and TTS is explored, highlighting the transition from traditional concatenative and parametric TTS to the contemporary neural TTS systems.

Introduction to AI and Neural Networks

Artificial Intelligence embodies the quest to imbue machines with the capability to mimic human intelligence. Neural networks, inspired by the biological neural networks in the human brain, serve as the foundation of many AI systems. These networks consist of layers of interconnected nodes or "neurons" that process input data, learning patterns and features through training.

Neural Networks in TTS

Neural TTS systems leverage deep learning architectures, such as CNNs and RNNs, to synthesize speech. CNNs are adept at handling spatial hierarchy in data, making them useful for extracting features from text. RNNs, particularly those with Long Short-Term Memory (LSTM) units, excel in processing sequential data, crucial for capturing the temporal dynamics of speech.

From Traditional to Neural TTS

Traditional TTS systems relied on concatenative and parametric methods, which pieced together pre-recorded speech segments or utilized mathematical models to generate speech. While effective, these methods often resulted in synthetic-sounding audio. The integration of neural networks into TTS has led to the development of systems that can produce highly natural and intelligible speech. Neural TTS systems generate waveform directly or use vocoders to convert spectral features into audio signals, leveraging the learned representations of speech patterns and features.

Conclusion

The integration of AI and neural networks into TTS technology has been transformative, enabling the creation of speech that closely resembles human voice in terms of naturalness, emotion, and clarity. As AI and neural network technologies continue to evolve, we anticipate further advancements in TTS systems, making them more accessible, customizable, and efficient. This evolution not only promises to enhance user experiences across various applications but also opens up new possibilities for human-computer interaction.