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Hands-On Natural Language Processing with Python by Rajalingappaa Shanmugamani, Rajesh Arumugam

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Summary

In this chapter, we described deep learning methods in speech recognition. We looked at an overview of speech recognition software currently used in practice. We showed that traditional HMM-based methods might need to incorporate specific language models, whereas neural network-based methods can learn end to end speech transcription entirely from data. This is one main advantage of neural network models over HMM models. We developed a basic spoken digits recognition model using TensorFlow. We then used the open spoken digits dataset to train and make predictions on a test set. This example provided the background of the tasks involved in a speech recognition system like extraction of the frequency spectrum like MFCC features from ...

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