Speaker Independent Speech Recognition Using Neural Network

In spite of the advances accomplished throughout the last few decades, automatic speech recognition (ASR) is still a challenging and difficult task when the systems are applied in the real world. Different requirements for various applications drive the researchers to explore for more effective ways in the particular application. Attempts to apply artificial neural networks (ANN) as a classification tool are proposed to increase the reliability of the system. This project studies the approach of using neural network for speaker independent isolated word recognition on small vocabularies and proposes a method to have a simple MLP as speech recognizer. Our approach is able to overcome the current limitations of MLP in the selection of input buffers’ size by proposing a method on frames selection. Linear predictive coding (LPC) has been applied to represent speech signal in frames in early stage. Features from the selected frames are used to train the multilayer perceptrons (MLP) feedforward back-propagation (FFBP) neural network during the training stage. Same routine has been applied to the speech signal during the recognition stage and the unknown test pattern will be classified to one of the nearest pattern. In short, the selected frames represent the local features of the speech signal and all of them contribute to the global similarity for the whole speech signal. The analysis, design and the PC based voice dialling system is developed using MATLAB®

Continuous speech phoneme recognition using neural networks and grammar correction.

by Wai-Tat Fu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 104-[109]).Chapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Problem of Speech Recognition --- p.1Chapter 1.2 --- Why continuous speech recognition? --- p.5Chapter 1.3 --- Current status of continuous speech recognition --- p.6Chapter 1.4 --- Research Goal --- p.10Chapter 1.5 --- Thesis outline --- p.10Chapter 2 --- Current Approaches to Continuous Speech Recognition --- p.12Chapter 2.1 --- BASIC STEPS FOR CONTINUOUS SPEECH RECOGNITION --- p.12Chapter 2.2 --- THE HIDDEN MARKOV MODEL APPROACH --- p.16Chapter 2.2.1 --- Introduction --- p.16Chapter 2.2.2 --- Segmentation and Pattern Matching --- p.18Chapter 2.2.3 --- Word Formation and Syntactic Processing --- p.22Chapter 2.2.4 --- Discussion --- p.23Chapter 2.3 --- NEURAL NETWORK APPROACH --- p.24Chapter 2.3.1 --- Introduction --- p.24Chapter 2.3.2 --- Segmentation and Pattern Matching --- p.25Chapter 2.3.3 --- Discussion --- p.27Chapter 2.4 --- MLP/HMM HYBRID APPROACH --- p.28Chapter 2.4.1 --- Introduction --- p.28Chapter 2.4.2 --- Architecture of Hybrid MLP/HMM Systems --- p.29Chapter 2.4.3 --- Discussions --- p.30Chapter 2.5 --- SYNTACTIC GRAMMAR --- p.30Chapter 2.5.1 --- Introduction --- p.30Chapter 2.5.2 --- Word formation and Syntactic Processing --- p.31Chapter 2.5.3 --- Discussion --- p.32Chapter 2.6 --- SUMMARY --- p.32Chapter 3 --- Neural Network As Pattern Classifier --- p.34Chapter 3.1 --- INTRODUCTION --- p.34Chapter 3.2 --- TRAINING ALGORITHMS AND TOPOLOGIES --- p.35Chapter 3.2.1 --- Multilayer Perceptrons --- p.35Chapter 3.2.2 --- Recurrent Neural Networks --- p.39Chapter 3.2.3 --- Self-organizing Maps --- p.41Chapter 3.2.4 --- Learning Vector Quantization --- p.43Chapter 3.3 --- EXPERIMENTS --- p.44Chapter 3.3.1 --- The Data Set --- p.44Chapter 3.3.2 --- Preprocessing of the Speech Data --- p.45Chapter 3.3.3 --- The Pattern Classifiers --- p.50Chapter 3.4 --- RESULTS AND DISCUSSIONS --- p.53Chapter 4 --- High Level Context Information --- p.56Chapter 4.1 --- INTRODUCTION --- p.56Chapter 4.2 --- HIDDEN MARKOV MODEL APPROACH --- p.57Chapter 4.3 --- THE DYNAMIC PROGRAMMING APPROACH --- p.59Chapter 4.4 --- THE SYNTACTIC GRAMMAR APPROACH --- p.60Chapter 5 --- Finite State Grammar Network --- p.62Chapter 5.1 --- INTRODUCTION --- p.62Chapter 5.2 --- THE GRAMMAR COMPILATION --- p.63Chapter 5.2.1 --- Introduction --- p.63Chapter 5.2.2 --- K-Tails Clustering Method --- p.66Chapter 5.2.3 --- Inference of finite state grammar --- p.67Chapter 5.2.4 --- Error Correcting Parsing --- p.69Chapter 5.3 --- EXPERIMENT --- p.71Chapter 5.4 --- RESULTS AND DISCUSSIONS --- p.73Chapter 6 --- The Integrated System --- p.81Chapter 6.1 --- INTRODUCTION --- p.81Chapter 6.2 --- POSTPROCESSING OF NEURAL NETWORK OUTPUT --- p.82Chapter 6.2.1 --- Activation Threshold --- p.82Chapter 6.2.2 --- Duration Threshold --- p.85Chapter 6.2.3 --- Merging of Phoneme boundaries --- p.88Chapter 6.3 --- THE ERROR CORRECTING PARSER --- p.90Chapter 6.4 --- RESULTS AND DISCUSSIONS --- p.96Chapter 7 --- Conclusions --- p.101Bibliography --- p.10

An autopoietic approach to the development of speech recognition (pendekatan autopoietic dalam pembangunan pengecaman suara)

The focus of research here is on the implementation of speech recognition through an autopoietic approach. The work done here has culminated in the introduction of a neural network architecture named Homunculus Network. This network was used in the development of a speech recognition system for Bahasa Melayu. The speech recognition system is an isolated-word, phoneme-level speech recognizer that is speaker independent and has a vocabulary of 15 words. The research done has identified some issues worth further work later. These issues are also the basis for the design and the development of the new autopoietic speech recognition system

An efficient implementation of lattice-ladder multilayer perceptrons in field programmable gate arrays

The implementation efficiency of electronic systems is a combination of conflicting requirements, as increasing volumes of computations, accelerating the exchange of data, at the same time increasing energy consumption forcing the researchers not only to optimize the algorithm, but also to quickly implement in a specialized hardware. Therefore in this work, the problem of efficient and straightforward implementation of operating in a real-time electronic intelligent systems on field-programmable gate array (FPGA) is tackled. The object of research is specialized FPGA intellectual property (IP) cores that operate in a real-time. In the thesis the following main aspects of the research object are investigated: implementation criteria and techniques. The aim of the thesis is to optimize the FPGA implementation process of selected class dynamic artificial neural networks. In order to solve stated problem and reach the goal following main tasks of the thesis are formulated: rationalize the selection of a class of Lattice-Ladder Multi-Layer Perceptron (LLMLP) and its electronic intelligent system test-bed – a speaker dependent Lithuanian speech recognizer, to be created and investigated; develop dedicated technique for implementation of LLMLP class on FPGA that is based on specialized efficiency criteria for a circuitry synthesis; develop and experimentally affirm the efficiency of optimized FPGA IP cores used in Lithuanian speech recognizer. The dissertation contains: introduction, four chapters and general conclusions. The first chapter reveals the fundamental knowledge on computer-aideddesign, artificial neural networks and speech recognition implementation on FPGA. In the second chapter the efficiency criteria and technique of LLMLP IP cores implementation are proposed in order to make multi-objective optimization of throughput, LLMLP complexity and resource utilization. The data flow graphs are applied for optimization of LLMLP computations. The optimized neuron processing element is proposed. The IP cores for features extraction and comparison are developed for Lithuanian speech recognizer and analyzed in third chapter. The fourth chapter is devoted for experimental verification of developed numerous LLMLP IP cores. The experiments of isolated word recognition accuracy and speed for different speakers, signal to noise ratios, features extraction and accelerated comparison methods were performed. The main results of the thesis were published in 12 scientific publications: eight of them were printed in peer-reviewed scientific journals, four of them in a Thomson Reuters Web of Science database, four articles – in conference proceedings. The results were presented in 17 scientific conferences

Early Human Vocalization Development: A Collection of Studies Utilizing Automated Analysis of Naturalistic Recordings and Neural Network Modeling

Understanding early human vocalization development is a key part of understanding the origins of human communication. What are the characteristics of early human vocalizations and how do they change over time? What mechanisms underlie these changes? This dissertation is a collection of three papers that take a computational approach to addressing these questions, using neural network simulation and automated analysis of naturalistic data.The first paper uses a self-organizing neural network to automatically derive holistic acoustic features characteristic of prelinguistic vocalizations. A supervised neural network is used to classify vocalizations into human-judged categories and to predict the age of the child vocalizing. The study represents a first step toward taking a data-driven approach to describing infant vocalizations. Its performance in classification represents progress toward developing automated analysis tools for coding infant vocalization types.The second paper is a computational model of early vocal motor learning. It adapts a popular type of neural network, the self-organizing map, in order to control a vocal tract simulator and in order to have learning be dependent on whether the model\u27s actions are reinforced. The model learns both to control production of sound at the larynx (phonation), an early-developing skill that is a prerequisite for speech, and to produce vowels that gravitate toward the vowels in a target language (either English or Korean) for which it is reinforced. The model provides a computationally-specified explanation for how neuromotor representations might be acquired in infancy through the combination of exploration, reinforcement, and self-organized learning.The third paper utilizes automated analysis to uncover patterns of vocal interaction between child and caregiver that unfold over the course of day-long, totally naturalistic recordings. The participants include 16- to 48-month-old children with and without autism. Results are consistent with the idea that there is a social feedback loop wherein children produce speech-related vocalizations, these are preferentially responded to by adults, and this contingency of adult response shapes future child vocalizations. Differences in components of this feedback loop are observed in autism, as well as with different maternal education levels

The Recommendation Architecture: Lessons from Large-Scale Electronic Systems Applied to Cognition

A fundamental approach of cognitive science is to understand cognitive systems by separating them into modules. Theoretical reasons are described which force any system which learns to perform a complex combination of real time functions into a modular architecture. Constraints on the way modules divide up functionality are also described. The architecture of such systems, including biological systems, is constrained into a form called the recommendation architecture, with a primary separation between clustering and competition. Clustering is a modular hierarchy which manages the interactions between functions on the basis of detection of functionally ambiguous repetition. Change to previously detected repetitions is limited in order to maintain a meaningful, although partially ambiguous context for all modules which make use of the previously defined repetitions. Competition interprets the repetition conditions detected by clustering as a range of alternative behavioural recommendations, and uses consequence feedback to learn to select the most appropriate recommendation. The requirements imposed by functional complexity result in very specific structures and processes which resemble those of brains. The design of an implemented electronic version of the recommendation architecture is described, and it is demonstrated that the system can heuristically define its own functionality, and learn without disrupting earlier learning. The recommendation architecture is compared with a range of alternative cognitive architectural proposals, and the conclusion reached that it has substantial potential both for understanding brains and for designing systems to perform cognitive functions