Chapters Brief Overview:

Speech processing-An introduction to the fundamental concepts in speech processing, setting the stage for deeper insights into the role of speech in robotics.

Neural network (machine learning)-Explores the core of machine learning and how neural networks are applied to robotic systems for decisionmaking and speech understanding.

Speech recognition-Discusses speech recognition technologies and their importance in enabling robots to interpret and respond to human speech.

Linear predictive coding-Delivers insights into predictive modeling techniques and their application in improving the accuracy of speech processing in robotics.

Vector quantization-Focuses on vector quantization methods and how they optimize speech data compression, ensuring faster and more efficient processing in robotic systems.

Hidden Markov model-Explains how Hidden Markov models are used to process sequential data, critical for tasks such as speech recognition and robotic motion.

Unsupervised learning-Describes unsupervised learning techniques that allow robots to learn from unstructured data without the need for labeled input.

Instantaneously trained neural networks-Examines the innovative concept of neural networks trained onthefly, making speech recognition systems more adaptive and responsive.

Boltzmann machine-Introduces Boltzmann machines and their application in probabilistic learning, enhancing the cognitive capabilities of robots.

Recurrent neural network-Explores the use of recurrent neural networks to handle temporal data, crucial for processing continuous speech input and improving robothuman interaction.

Channel state information-Provides an overview of how channel state information influences speech transmission and recognition in robotic systems, ensuring clear communication.

Long shortterm memory-Discusses long shortterm memory networks, a breakthrough in training robots to retain and process complex speech data over time.

Activation function-Analyzes the role of activation functions in neural networks and how they help robots process speech data efficiently.

Activity recognition-Covers how activity recognition methods allow robots to interpret human actions, vital for enhancing interaction and autonomy.

Timeinhomogeneous hidden Bernoulli model-Explains the timeinhomogeneous Bernoulli model and its relevance in sequential learning tasks like speech processing.

Entropy estimation-Details how entropy estimation techniques are applied to speech processing in robotics, ensuring the systems make more informed decisions.

Types of artificial neural networks-Provides an overview of different types of neural networks and their specific applications in robotics and speech processing.

Deep learning-Discusses deep learning methods and their impact on advancing speech processing, making robotic systems smarter and more responsive.

Yasuo Matsuyama-Honors the contributions of Yasuo Matsuyama, a pioneer in speech processing and robotics, whose work continues to inspire innovation.

Convolutional neural network-Introduces convolutional neural networks and their critical role in speech recognition and robotic vision systems.

Perceptron-Explains the perceptron, the foundational neural network model, and its continued relevance in speech recognition systems.