Speech Processing: Advances in Human Robot Communication and Interaction

By Fouad Sabry

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.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Dec 28, 2024

Book preview

Chapter 1: Speech processing

The study of voice signals and the various techniques for processing signals is referred to as speech processing. As a result of the signals being processed in a digital form most of the time, speech processing may be thought of as a subset of digital signal processing that is applied specifically to speech signals. The capture, modification, storage, transport, and output of speech signals are all components that make up aspects of speech processing. Speech recognition is the term used to describe the input, while speech synthesis is the term used to describe the output.

Early efforts at speech processing and recognition concentrated largely on deciphering a limited number of fundamental phonetic components, such as vowels. In the year 1952, Stephen Balashek, R. Biddulph, and K. H. Davis, three researchers working at Bell Labs, devised a system that could distinguish digits that were uttered by a single individual.

By the early 2000s, the predominant speech processing technique had begun its transition away from Hidden Markov Models and toward more sophisticated neural networks and deep learning. This move occurred in response to the rise of natural language processing.

An approach known as dynamic time warping, or DTW, is used to measure the degree of resemblance between two temporal sequences, each of which might move at a different pace. DTW is a technique that, in general, is used to determine an ideal match between two provided sequences (for example, time series) in accordance with certain restrictions and constraints. The optimal match is the one that satisfies all of the restrictions and the rules as well as having the lowest cost, where the cost is computed as the sum of absolute differences between the values of each matched pair of indices. This sum is then divided by the number of indices that are being matched.

It is possible to express a hidden Markov model as the most basic form of a dynamic Bayesian network. Given a set of observations y, the purpose of the procedure is to provide an estimate for a hidden variable denoted by x(t) (t). The conditional probability distribution of the hidden variable x(t) at time t, given the values of the hidden variable x at all times, depends only on the value of the hidden variable x(t 1) when the Markov property is applied. This is because the values of the hidden variable x at all times are used to determine the conditional probability distribution. Likewise, the value of the observable variable y(t) solely relies on the value of the hidden variable x(t) at any given point in time (both at time t).

An artificial neural network, also known as an ANN, is built on top of a collection of interconnected units or nodes that are referred to as artificial neurons. These neurons are meant to roughly imitate the neurons that are found in a real brain. A signal may be sent from one artificial neuron to another through each link, much like the synapses in a real brain. When it receives a signal, an artificial neuron has the ability to process it and then pass on the information to other artificial neurons that are linked to it. In the majority of ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. In other words, the signal at a connection between artificial neurons is a real number.

Phase is often thought to be a random variable that is uniform, and as a result, it is worthless.

This is due wrapping of phase: result of arctangent function is not continuous due to periodical jumps on

2\pi

.

After phase unwrapping (see,

{\displaystyle \phi (h,l)=\phi _{lin}(h,l)+\Psi (h,l)}

, where

{\displaystyle \phi _{lin}(h,l)=\omega _{0}(l'){}_{\Delta }t}

is linear phase (

{\displaystyle {}_{\Delta }t}

is temporal shift at each frame of analysis),

{\displaystyle \Psi (h,l)}

is phase contribution of the vocal tract and phase source.

The obtained phase estimates have several applications for noise reduction, including the following: instantaneous phase that has been temporally smoothed out

Interactive Voice Systems

Virtual Assistants

Voice Identification

Emotion Recognition

Call Center Automation

Robotics

{End Chapter 1}

Chapter 2: Neural network (machine learning)

A neural network, also known as an artificial neural network or neural net, and known by its abbreviations ANN or NN, is a model that is used in machine learning. This model is inspired by the form and function of biological neural networks seen in animal brains.

The artificial neurons that make up an artificial neural network (ANN) are connected units or nodes that are modeled after the neurons that are found in the brain. The edges that connect these are a representation of the synapses that are found in the brain. After receiving signals from other neurons that are connected to it, each artificial neuron processes those signals and then transmits them to other neurons that are connected to it. The signal is a real number, and the activation function is a non-linear function that operates on the total of the inputs to each neuron. This function is responsible for computing the output of each neuron. During the process of learning, a weight is used to determine the strength of the signal at each link. This weight is adjusted and adjusted as necessary.

In a typical situation, neurons are grouped together into layers. It is possible for various layers to each conduct a unique transformation on their respective inputs. It is possible for signals to go via numerous intermediate layers, also known as hidden layers, as they make their way from the first layer, which is the input layer, to the last layer, which is the output layer. When a network includes at least two hidden layers, it is often referred to as a deep neural network to describe the network.

There are several applications for artificial neural networks, including predictive modeling, adaptive control, and the resolution of issues in artificial intelligence. There are also many other applications. They have the ability to gain knowledge through experience and can draw inferences from a collection of facts that appears to be unrelated to one another.

The majority of the time, neural networks are trained through the process of empirical risk limitation. This approach is predicated on the concept of optimizing the parameters of the network in order to minimize the difference, also known as the empirical risk, between the anticipated output and the actual target values in a particular dataset. Estimation of the parameters of the network is often accomplished by the utilization of gradient-based approaches such as backpropagation. Artificial neural networks (ANNs) learn from labeled training data during the training phase by iteratively changing its parameters in order to minimize a loss function that has been set. Using this strategy, the network is able to generalize to data that it has not previously encountered.

More than two centuries ago, early work in statistics laid the foundation for the deep neural networks that are used today. The most basic type of feedforward neural network (FNN) is a linear network. This type of network is made up of a single layer of output nodes that have linear activation functions. The inputs are sent directly to the outputs through a series of weights. Calculations are performed at each node to determine the total of the products of the weights and the inputs. For the purpose of minimizing the mean squared errors that occur between these estimated outputs and the goal values that have been provided, an adjustment to the weights has been created. Both the method of least squares and linear regression are names that have been used to refer to this technique for more than two centuries. Legendre (1805) and Gauss (1795) utilized it as a method for determining a decent rough linear fit to a set of points in order to make predictions regarding the movement of the planets.

The operation of digital computers, such as the von Neumann architecture, has traditionally been accomplished through the execution of explicit instructions, with memory being accessed by a number of discrete processors. Certain neural networks, on the other hand, were initially developed as a result of an attempt to describe the information processing that occurs in biological systems by utilizing the connectionism framework. Connectionist computing, in contrast to the von Neumann approach, does not divide memory and processing into two distinct categories.

In 1943, Warren McCulloch and Walter Pitts examined the possibility of devising a computational model for neural networks that did not include learning. The research was able to be divided into two distinct methodologies as a result of this model. The first method concentrated on biological processes, while the second method was centered on the utilization of neural networks in the field of artificial intelligence science.

D. was born in the late 1940s. Hebbian learning is a learning hypothesis that was proposed by O. Hebb. This hypothesis was founded on the mechanism of brain plasticity and developed into a theory. Numerous early neural networks, such as Rosenblatt's perceptron and the Hopfield network, made use of it in their operations. In 1954, Farley and Clark carried out a simulation of a Hebbian network by utilizing computing devices. Rochester, Holland, Habit, and Duda (1956) were the ones who came up with further neural network computational machines.

The United States Office of Naval Research provided funding for the development of the perceptron, which was one of the first artificial neural networks to be developed. The perceptron was described by psychologist Frank Rosenblatt in the year 1958.

An even earlier perceptron-like device was developed by Farley and Clark, according to R. D. Joseph (1960). He writes that Farley and Clark of MIT Lincoln Laboratory actually preceded Rosenblatt in the development of a perceptron-like device. However, they dropped the subject.

As a result of the perceptron, public interest in research pertaining to artificial neural networks increased, which led to a significant increase in funding from the United States government. The Golden Age of AI was fueled by the optimistic claims made by computer scientists on the ability of perceptrons to replicate human intelligence. This circumstance helped to the development of artificial