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 the raw audio data and converting the text transcripts to labels. We then introduced the DeepSpeech architecture from Baidu, which is one of...