Reconfigurable Computing for Speech Recognition: Preliminary Findings

Continuous real-time speech recognition is a highly computationally-demanding task, but one which can take good advantage of a parallel processing system. To this end, we describe proposals for, and preliminary findings of, research in implementing in programmable logic the decoder part of a speech recognition system. Recognition via Viterbi decoding of Hidden Markov Models is outlined, along with details of current implementations, which aim to exploit properties of the algorithm that could make it well-suited for devices such as FPGAs. The question of how to deal with limited resources, by reconfiguration or otherwise, is also addressed

Implementation of boolean neural networks on parallel computers

This paper analyses the parallel implementation using networks of transputers of a neural structure belonging to a particular class of neural architectures known as GSN neural networks. These architectures, belonging to the general clasa of RAM-based networks and composed 01 digitally specified processing nodes, have been implemented using different processing topologies, and performance in relatíon to both training and testing efficiency in a practical pattern recognition task has been evaluated.Eje: Redes Neuronales. Algoritmos genéticosRed de Universidades con Carreras en Informática (RedUNCI

Multi-transputer based isolated word speech recognition system.

by Francis Cho-yiu Chik.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 129-135).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Automatic speech recognition and its applications --- p.1Chapter 1.1.1 --- Artificial Neural Network (ANN) approach --- p.3Chapter 1.2 --- Motivation --- p.5Chapter 1.3 --- Background --- p.6Chapter 1.3.1 --- Speech recognition --- p.6Chapter 1.3.2 --- Parallel processing --- p.7Chapter 1.3.3 --- Parallel architectures --- p.10Chapter 1.3.4 --- Transputer --- p.12Chapter 1.4 --- Thesis outline --- p.13Chapter 2 --- Speech Signal Pre-processing --- p.14Chapter 2.1 --- Determine useful signal --- p.14Chapter 2.1.1 --- End point detection using energy --- p.15Chapter 2.1.2 --- End point detection enhancement using zero crossing rate --- p.18Chapter 2.2 --- Pre-emphasis filter --- p.19Chapter 2.3 --- Feature extraction --- p.20Chapter 2.3.1 --- Filter-bank spectrum analysis model --- p.22Chapter 2.3.2 --- Linear Predictive Coding (LPC) coefficients --- p.25Chapter 2.3.3 --- Cepstral coefficients --- p.27Chapter 2.3.4 --- Zero crossing rate and energy --- p.27Chapter 2.3.5 --- Pitch (fundamental frequency) detection --- p.28Chapter 2.4 --- Discussions --- p.30Chapter 3 --- Speech Recognition Methods --- p.32Chapter 3.1 --- Template matching using Dynamic Time Warping (DTW) --- p.32Chapter 3.2 --- Hidden Markov Model (HMM) --- p.37Chapter 3.2.1 --- Vector Quantization (VQ) --- p.38Chapter 3.2.2 --- Description of a discrete HMM --- p.41Chapter 3.2.3 --- Probability evaluation --- p.42Chapter 3.2.4 --- Estimation technique for model parameters --- p.46Chapter 3.2.5 --- State sequence for the observation sequence --- p.48Chapter 3.3 --- 2-dimensional Hidden Markov Model (2dHMM) --- p.49Chapter 3.3.1 --- Calculation for a 2dHMM --- p.50Chapter 3.4 --- Discussions --- p.56Chapter 4 --- Implementation --- p.59Chapter 4.1 --- Transputer based multiprocessor system --- p.59Chapter 4.1.1 --- Transputer Development System (TDS) --- p.60Chapter 4.1.2 --- System architecture --- p.61Chapter 4.1.3 --- Transtech TMB16 mother board --- p.62Chapter 4.1.4 --- Farming technique --- p.64Chapter 4.2 --- Farming technique on extracting spectral amplitude feature --- p.68Chapter 4.3 --- Feature extraction for LPC --- p.73Chapter 4.4 --- DTW based recognition --- p.77Chapter 4.4.1 --- Feature extraction --- p.77Chapter 4.4.2 --- Training and matching --- p.78Chapter 4.5 --- HMM based recognition --- p.80Chapter 4.5.1 --- Feature extraction --- p.80Chapter 4.5.2 --- Model training and matching --- p.81Chapter 4.6 --- 2dHMM based recognition --- p.83Chapter 4.6.1 --- Feature extraction --- p.83Chapter 4.6.2 --- Training --- p.83Chapter 4.6.3 --- Recognition --- p.87Chapter 4.7 --- Training convergence in HMM and 2dHMM --- p.88Chapter 4.8 --- Discussions --- p.91Chapter 5 --- Experimental Results --- p.92Chapter 5.1 --- "Comparison of DTW, HMM and 2dHMM" --- p.93Chapter 5.2 --- Comparison between HMM and 2dHMM --- p.98Chapter 5.2.1 --- Recognition test on 20 English words --- p.98Chapter 5.2.2 --- Recognition test on 10 Cantonese syllables --- p.102Chapter 5.3 --- Recognition test on 80 Cantonese syllables --- p.113Chapter 5.4 --- Speed matching --- p.118Chapter 5.5 --- Computational performance --- p.119Chapter 5.5.1 --- Training performance --- p.119Chapter 5.5.2 --- Recognition performance --- p.120Chapter 6 --- Discussions and Conclusions --- p.126Bibliography --- p.129Chapter A --- An ANN Model for Speech Recognition --- p.136Chapter B --- A Speech Signal Represented in Fequency Domain (Spectrogram) --- p.138Chapter C --- Dynamic Programming --- p.144Chapter D --- Markov Process --- p.145Chapter E --- Maximum Likelihood (ML) --- p.146Chapter F --- Multiple Training --- p.149Chapter F.1 --- HMM --- p.150Chapter F.2 --- 2dHMM --- p.150Chapter G --- IMS T800 Transputer --- p.152Chapter G.1 --- IMS T800 architecture --- p.152Chapter G.2 --- Instruction encoding --- p.153Chapter G.3 --- Floating point instructions --- p.155Chapter G.4 --- Optimizing use of the stack --- p.157Chapter G.5 --- Concurrent operation of FPU and CPU --- p.15

On the synthesis and processing of high quality audio signals by parallel computers

This work concerns the application of new computer architectures to the creation and manipulation of high-quality audio bandwidth signals. The configuration of both the hardware and software in such systems falls under consideration in the three major sections which present increasing levels of algorithmic concurrency. In the first section, the programs which are described are distributed in identical copies across an array of processing elements; these programs run autonomously, generating data independently, but with control parameters peculiar to each copy: this type of concurrency is referred to as isonomic}The central section presents a structure which distributes tasks across an arbitrary network of processors; the flow of control in such a program is quasi- indeterminate, and controlled on a demand basis by the rate of completion of the slave tasks and their irregular interaction with the master. Whilst that interaction is, in principle, deterministic, it is also data-dependent; the dynamic nature of task allocation demands that no a priori knowledge of the rate of task completion be required. This type of concurrency is called dianomic? Finally, an architecture is described which will support a very high level of algorithmic concurrency. The programs which make efficient use of such a machine are designed not by considering flow of control, but by considering flow of data. Each atomic algorithmic unit is made as simple as possible, which results in the extensive distribution of a program over very many processing elements. Programs designed by considering only the optimum data exchange routes are said to exhibit systolic^ concurrency. Often neglected in the study of system design are those provisions necessary for practical implementations. It was intended to provide users with useful application programs in fulfilment of this study; the target group is electroacoustic composers, who use digital signal processing techniques in the context of musical composition. Some of the algorithms in use in this field are highly complex, often requiring a quantity of processing for each sample which exceeds that currently available even from very powerful computers. Consequently, applications tend to operate not in 'real-time' (where the output of a system responds to its input apparently instantaneously), but by the manipulation of sounds recorded digitally on a mass storage device. The first two sections adopt existing, public-domain software, and seek to increase its speed of execution significantly by parallel techniques, with the minimum compromise of functionality and ease of use. Those chosen are the general- purpose direct synthesis program CSOUND, from M.I.T., and a stand-alone phase vocoder system from the C.D.P..(^4) In each case, the desired aim is achieved: to increase speed of execution by two orders of magnitude over the systems currently in use by composers. This requires substantial restructuring of the programs, and careful consideration of the best computer architectures on which they are to run concurrently. The third section examines the rationale behind the use of computers in music, and begins with the implementation of a sophisticated electronic musical instrument capable of a degree of expression at least equal to its acoustic counterparts. It seems that the flexible control of such an instrument demands a greater computing resource than the sound synthesis part. A machine has been constructed with the intention of enabling the 'gestural capture' of performance information in real-time; the structure of this computer, which has one hundred and sixty high-performance microprocessors running in parallel, is expounded; and the systolic programming techniques required to take advantage of such an array are illustrated in the Occam programming language

Real-time processing of radar return on a parallel computer

NASA is working with the FAA to demonstrate the feasibility of pulse Doppler radar as a candidate airborne sensor to detect low altitude windshears. The need to provide the pilot with timely information about possible hazards has motivated a demand for real-time processing of a radar return. Investigated here is parallel processing as a means of accommodating the high data rates required. A PC based parallel computer, called the transputer, is used to investigate issues in real time concurrent processing of radar signals. A transputer network is made up of an array of single instruction stream processors that can be networked in a variety of ways. They are easily reconfigured and software development is largely independent of the particular network topology. The performance of the transputer is evaluated in light of the computational requirements. A number of algorithms have been implemented on the transputers in OCCAM, a language specially designed for parallel processing. These include signal processing algorithms such as the Fast Fourier Transform (FFT), pulse-pair, and autoregressive modelling, as well as routing software to support concurrency. The most computationally intensive task is estimating the spectrum. Two approaches have been taken on this problem, the first and most conventional of which is to use the FFT. By using table look-ups for the basis function and other optimizing techniques, an algorithm has been developed that is sufficient for real time. The other approach is to model the signal as an autoregressive process and estimate the spectrum based on the model coefficients. This technique is attractive because it does not suffer from the spectral leakage problem inherent in the FFT. Benchmark tests indicate that autoregressive modeling is feasible in real time

The Application of Vector Quantization to Image Compression for Satellite Imaging Systems

Over the past few years it has become more and more common to use satellites for imaging applications. The imaging may be performed in the visual realm using cameras, or may infrared or electro-optical using multi-sensor arrays. One thing that all these imaging techniques have in common is the large amount of data that must be stored and/or transmitted per image. If many images are being gathered over a relatively short time, then storage and power requirements may become too large for small satellite systems

Centre for Information Science Research Annual Report, 1987-1991

Annual reports from various departments of the AN