Abstract
Speech synthesis and voice recognition are two critical areas of research in the field of artificial intelligence and human-computer interaction. Neural networks have revolutionized these fields by enabling more natural and efficient synthesis of human-like speech and accurate voice recognition systems. This article explores the role of neural networks in speech synthesis (text-to-speech, TTS) and voice recognition (automatic speech recognition, ASR). It highlights different neural network architectures, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformer models, and their applications in these tasks. Furthermore, the article examines the challenges, advancements, and future trends in the integration of neural networks in speech synthesis and voice recognition systems.
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