Thesis (MSc)--University of Stellenbosch, 2005. / Bibliography. Also available via the Internet.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 116-117). Also available on the Internet.

In this thesis I will be concerned with linking the observed speechsignal to the configuration of articulators.Due to the potentially rapid motion of the articulators, the speechsignal can be highly non-stationary. The typical linear analysistechniques that assume quasi-stationarity may not have sufficienttime-frequency resolution to determine the place of articulation.I argue that the traditional low and high-level primitives of speechprocessing, frequency and phonemes, are inadequate and should bereplaced by a representation with three layers: 1. short pitch periodresonances and other spatio-temporal patterns 2. articulatorconfiguration trajectories 3. syllables. The patterns indicatearticulator configuration trajectories (how the tongue, jaws, etc. aremoving), which are interpreted as syllables and words.My patterns are an alternative to frequency. I use shorttime-domain features of the sound waveform, which can be extractedfrom each vowel pitch period pattern, to identify the positions of thearticulators with high reliability. These features are importantbecause by capitalizing on detailed measurements within a single pitchperiod, the rapid articulator movements can be tracked. No linearsignal processing approach can achieve the combination of sensitivityto short term changes and measurement accuracy resulting from thesenonlinear techniques.The measurements I use are neurophysiologically plausible: theauditory system could be using similar methods.I have demonstrated this approach by constructing a robust techniquefor categorizing the English voiced stops as the consonants B, D, or Gbased on the vocalic portions of their releases. The classificationrecognizes 93.5%, 81.8% and 86.1% of the b, d and gto ae transitions with false positive rates 2.9%, 8.7% and2.6% respectively.

Thesis (MScEng)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: The syllable has been proposed as a unit of automatic speech recognition due to its strong links with human speech production and perception. Recently, it has been proved that incorporating information from syllable-length time-scales into automatic speech recognition improves results in large vocabulary recognition tasks. It was also shown to aid in various language recognition tasks and in foreign accent identification. Therefore, the ability to automatically segment speech into syllables is an important research tool. Where most previous studies employed knowledge-based methods, this study presents a purely statistical method for the automatic syllabification of speech. We introduce the concept of hierarchical hidden Markov model structures and show how these can be used to implement a purely acoustical syllable segmenter based, on general sonority theory, combined with some of the phonotactic constraints found in the English language. The accurate reporting of syllabification results is a problem in the existing literature. We present a well-defined dynamic time warping (DTW) distance measure used for reporting syllabification results. We achieve a token error rate of 20.3% with a 42ms average boundary error on a relatively large set of data. This compares well with previous knowledge-based and statistically- based methods. / AFRIKAANSE OPSOMMING: Die syllabe is voorheen voorgestel as 'n basiese eenheid vir automatiese spraakherkenning weens die sterk verwantwskap wat dit het met spraak produksie en persepsie. Onlangs is dit bewys dat die gebruik van informasie van syllabe-lengte tydskale die resultate verbeter in groot woordeskat herkennings take. Dit is ook bewys dat die gebruik van syllabes automatiese taalherkenning en vreemdetaal aksent herkenning vergemaklik. Dit is daarom belangrik om vir navorsingsdoeleindes syllabes automaties te kan segmenteer. Vorige studies het kennisgebaseerde metodes gebruik om hierdie segmentasie te bewerkstellig. Hierdie studie gebruik 'n suiwer statistiese metode vir die automatiese syllabifikasie van spraak. Ons gebruik die konsep van hierargiese verskuilde Markov model strukture en wys hoe dit gebruik kan word om 'n suiwer akoestiese syllabe segmenteerder te implementeer. Die model word gebou deur dit te baseer op die teorie van sonoriteit asook die fonotaktiese beperkinge teenwoordig in die Engelse taal. Die akkurate voorstelling van syllabifikasie resultate is problematies in die bestaande literatuur. Ons definieer volledig 'n DTW (Dynamic Time Warping) afstands funksie waarmee ons ons syllabifikasie resultate weergee. Ons behaal 'n TER (Token Error Rate) van 20.3% met 'n 42ms gemiddelde grens fout op 'n relatiewe groot stel data. Dit vergelyk goed met vorige kennis-gebaseerde en statisties-gebaseerde metodes.

M.Ing. (Electrical and Electronic Engineering) / The feasibility of modulation and demodulation of speech signals within a microprocessor is invertigated in the following study. Existing modulation and demodulation techniques are investigated and new techniques. suitable for microprocessor implementation, described. Finally a single sideband demodulator was built using the TMS32010 microprocessor with results being better or comparable than existing analog techniques.

No description available.

Human interfaces to computers continue to be an active area of research. The keyboard is considered the basic interface for editing control as well as text input. Problems of correct typing and typing speed have urged research for alternative means for keyboard replacement, or at least "resizing" its monopoly. Pointing devices (e.g. a mouse) have been developed, and supporting software with icons is now widely used. Two other means are being developed and operationally tested, namely, the pen for handwriting text, commands and drawings, and spoken language interface, which is the subject of this thesis. Human/computer interface is an interactive man-machine communication facility that enjoys the following advantages. • High input speed: some experiments reveal that the rate of information input by speech is three times faster than keyboard input and eight times faster than inputting characters by hand. • No training needed: because the generation of speech is a very natural human action, it requires no special training. • Parallel processing with other information: production of speech works quite well in conjunction with gestures of hands and feet for visual perception of information. • Simple and economical input sensor: microphones are inexpensive and are readily available. • Coping with handicaps: these interfaces can be used in unusual circumstances of darkness, blindness, or other visual handicap. This dissertation presents a design of a Human Computer Interface (HCI) system that can be trained to work with an individual speaker. A new approach is introduced to extract key voice features, called Median Linear Predictive Coding (MLPC). MLPC reduces the HCI calculation time and gives an improved recognition rate. This design eliminated the typical Multi-layer Perceptron (MLP) problems of complexity growth with vocabulary size, the large training times required and the need for complete re-training whenever the vocabulary is extended. A novel modular neural network architecture, called a Pyramidal Modular Neural Network (PMNN), is introduced for recursive speech identification. In addition, many other system algorithms/components, such as speech endpoint detection, automatic noise thresholding, etc., must be tailored correctly in order to achieve high recognition accuracy. / Ph. D.

Automatic speech recognition systems have at last advanced to the state that they are now a feasible alternative for human-machine communication in selected applications. As such, research efforts are now beginning to focus on characteristics of the human, the recognition device, and the interface which optimize the system performance, rather than the previous trend of determining factors affecting recognizer performance alone. This study investigated two characteristics of the recognition device, the accuracy level at which it recognizes speech, and the vocabulary size of the recognizer as a percent of task vocabulary size to determine their effects on system performance. In addition, the study considered one characteristic of the user, age. Briefly, subjects performed a data entry task under each of the treatment conditions. Task completion time and the number of errors remaining at the end of each session were recorded. After each session, subjects rated the recognition device used as to its acceptability for the task. The accuracy level at which the recognizer was performing significantly influenced the task completion time as well as the user's acceptability ratings, but had only a small effect on the number of errors left uncorrected. The available vocabulary size also significantly affected the task completion time; however, its effect on the final error rate and on the acceptability ratings was negligible. The age of the subject was also found to influence both objective and subjective measures. Older subjects in general required longer times to complete the tasks; however, they consistently rated the speech input systems more favorably than the younger subjects. / Master of Science

In this thesis, we are interested in processing noisy speech signals that are meant to be heard by humans, and hence we approach the noise-suppression problem from a perceptual perspective. We develop a noise-suppression paradigm that is based on a model of the human auditory system, where we process signals in a way that is natural to the human ear. Under this paradigm, we transform an audio signal in to a perceptual domain, and processes the signal in this perceptual domain. This approach allows us to reduce the background noise and the audible artifacts that are seen in traditional noise-suppression algorithms, while preserving the quality of the processed speech. We develop a single- and dual-microphone algorithm based on this perceptual paradigm, and conduct subjecting tests to show that this approach outperforms traditional noise-suppression techniques. Moreover, we investigate the cause of audible artifacts that are generated as a result of suppressing the noise in noisy signals, and introduce constraints on the noise-suppression gain such that these artifacts are reduced.

Speech recognition systems have been implemented using a wide range of signal processing techniques including neuromorphic/biological inspired and Digital Signal Processing techniques. Neuromorphic/biologically inspired techniques, such as silicon cochlea models, are based on fairly simple yet highly parallel computation and/or computational units. While the area of digital signal processing (DSP) is based on block transforms and statistical or error minimization methods. Essential to each of these techniques is the first stage of extracting meaningful information from the speech signal, which is known as feature extraction. This can be done using biologically inspired techniques such as silicon cochlea models, or techniques beginning with a model of speech production and then trying to separate the the vocal tract response from an excitation signal. Even within each of these approaches, there are multiple techniques including cepstrum filtering, which sits under the class of Homomorphic signal processing, or techniques using FFT based predictive approaches. The underlying reality is there are multiple techniques that have attacked the problem in speech recognition but the problem is still far from being solved. The techniques that have shown to have the best recognition rates involve Cepstrum Coefficients for the feature extraction and Hidden-Markov Models to perform the pattern recognition. The presented research develops an analog system based on programmable analog array technology that can perform the initial stages of auditory feature extraction and recognition before passing information to a digital signal processor. The goal being a low power system that can be fully contained on one or more integrated circuit chips. Results show that it is possible to realize advanced filtering techniques such as Cepstrum Filtering and Vector Quantization in analog circuitry. Prior to this work, previous applications of analog signal processing have focused on vision, cochlea models, anti-aliasing filters and other single component uses. Furthermore, classic designs have looked heavily at utilizing op-amps as a basic core building block for these designs. This research also shows a novel design for a Hidden Markov Model (HMM) decoder utilizing circuits that take advantage of the inherent properties of subthreshold transistors and floating-gate technology to create low-power computational blocks.