Deep Neural Networks for Acoustic Modeling in Speech Recognition

This paper explores the use of deep neural networks (DNNs) for acoustic modeling in automatic speech recognition (ASR). It demonstrates that DNNs can significantly outperform traditional Gaussian Mixture Models (GMMs) in capturing the complex relationships between speech sounds and their acoustic representations, leading to improved speech recognition accuracy. The research highlights the advantages of using DNNs to learn hierarchical representations of speech data and their ability to generalize well to unseen data, making them a powerful tool for building more robust and accurate speech recognition systems.