Speech recognition is a complex task as it has to consider several sources of variation in the data. Some of these are variations in the speaker, the size of the vocabulary, ambient noise, accent, speaker characteristics, and so on. For example, one person would be saying a word like apple very fast compared to another who might be saying it more slowly like app........le. In both cases, the speech recognition system should produce the word apple. The most common approach to speech recognition uses Hidden Markov Models (HMMs) since speech data can be considered as a stochastic or probabilistic process, and for a short time, slices can be considered to be independent of time. The HMM model can be trained on short slices of the speech data that represents a phoneme or word. It can then predict the next phoneme or word that can be combined together...
Hands-On Natural Language Processing with Python
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Hands-On Natural Language Processing with Python
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Overview of this book
Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges.
To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow.
By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts.
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Hands-On Natural Language Processing with Python
Table of Contents (15 chapters)
Preface
Free Chapter
Getting Started
Text Classification and POS Tagging Using NLTK
Deep Learning and TensorFlow
Semantic Embedding Using Shallow Models
Text Classification Using LSTM
Searching and DeDuplicating Using CNNs
Named Entity Recognition Using Character LSTM
Text Generation and Summarization Using GRUs
Question-Answering and Chatbots Using Memory Networks
Machine Translation Using the Attention-Based Model
Speech Recognition Using DeepSpeech
Text-to-Speech Using Tacotron
Deploying Trained Models
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