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Automatic Speech Recognition: A Deep Learning Approach [Signals and Communicatio

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Automatic Speech Recognition (ASR) is a crucial technology that enables machines to understand and transcribe human speech. In recent years, deep learning approaches have revolutionized the field of ASR, achieving state-of-the-art performance on various speech recognition tasks.

Deep learning models, such as deep neural networks (DNNs) and recurrent neural networks (RNNs), have shown great success in handling the complexity of speech signals and capturing the underlying patterns in speech data. These models can learn hierarchical representations of speech features, making them more robust and accurate in transcribing spoken language.

One of the key advantages of deep learning for ASR is its ability to automatically extract relevant features from raw speech signals, eliminating the need for manual feature engineering. This allows the models to adapt and generalize well to different languages, accents, and speakers.

In addition, deep learning models can be trained on large amounts of speech data, which helps improve their performance and robustness. This data-driven approach enables the models to learn from a diverse range of speech samples, leading to better recognition accuracy and efficiency.

Overall, deep learning has significantly advanced the field of Automatic Speech Recognition, making it more accurate, reliable, and scalable. As the technology continues to evolve, we can expect even more innovative approaches and applications in the future.
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