Communication Sciences and Disorders Commons^™

Eeg-Based Processing And Classification Methodologies For Autism Spectrum Disorder: A Review, Gunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents …

Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya

Computer Science Faculty Publications

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …

Ua12/2/2 Talisman: Movement, Wku Student Affairs

WKU Administration Documents

2018 Talisman yearbook.

• Good, Hannah. Movement
• Kinser, Nicholas. Tunnel Trap
• Cozer, Claire. A Day in the Life of a Food Truck – Mike Wilson, Pop’s Street Eats
• Fletcher, Griffin. Beauty in Power – WKU Women’s Rugby Club
• Gordon, Zora. The Mixed Experience
• Hornsby, Morgan. Bonfire
• Waters, Adrianna. Mispoken – Communication Disorders
• Chu, Phi. Home Base – Jessica Barks
• Cooksey, Catrina. Rerouted – Sydney Clark, Austin Clark, Blake Perkins, Sheila Flener, Handicapped Persons
• Good, Hannah. Not Safe for Work – Prostitution
• Chu, Phi. Transfigured Night
• Carter, De’inara. Passing the Plate – International Students, Recipes
• Robb, Hayley. From Sole to Soul – …

Non-Locality, Precognition & Spirit From The Physics Point Of View, Florentin Smarandache, Victor Christianto

Branch Mathematics and Statistics Faculty and Staff Publications

There are various supernatural phenomena which can hardly be explained by the existing mainstream science, for instance non-local interactions (e.g. ESP) and also precognitive interdictions. And there are other problems such as how to include the Spirit in the framework of physics. For example, it has been known for long time that intuition plays significant role in many professions and human life, including entrepreneurship, government, and also in detective or law enforcement activities. Despite these examples, such a precognitive interdiction is hardly accepted in mainstream science. In this paper, we discuss non-local interactions and advanced solutions of Maxwell equations, and …

A Sketch Of Consciousness Space Beyond Freudian Mental Model And Implications To Socio-Economics Modeling And Integrative Cancer Therapy, Florentin Smarandache, Victor Christianto

Branch Mathematics and Statistics Faculty and Staff Publications

In this paper, we give an outline of an ongoing study to go beyond Freudian mental archetypal model. First, we discuss the essence of numerous problems that we suffer in our sophisticated and modernized society. Then we discuss possibility to reintroduce spirit into human consciousness. While we are aware that much remain to be done and we admit that this is only a sketch, we hope that this paper will start a fresh approach of research towards more realistic nonlinear consciousness model with wide ranging implications to socio-economic modeling and also integrative cancer therapy. At the last section we also …

Speech Processing Approach For Diagnosing Dementia In An Early Stage, Roozbeh Sadeghian, J. David Schaffer, Stephen A. Zahorian

Faculty Works

The clinical diagnosis of Alzheimer’s disease and other dementias is very challenging, especially in the early stages. Our hypothesis is that any disease that affects particular brain regions involved in speech production and processing will also leave detectable finger prints in the speech. Computerized analysis of speech signals and computational linguistics have progressed to the point where an automatic speech analysis system is a promising approach for a low-cost non-invasive diagnostic tool for early detection of Alzheimer’s disease.

We present empirical evidence that strong discrimination between subjects with a diagnosis of probable Alzheimer’s versus matched normal controls can be achieved …

On Syntropy & Precognitive Interdiction Based On Wheeler-Feynman’S Absorber Theory, Florentin Smarandache, Victor Christianto, Yunita Umniyati

Branch Mathematics and Statistics Faculty and Staff Publications

It has been known for long time that intuition plays significant role in many professions and human life, including in entrepreneurship, government, and also in detective or law enforcement activities. Women are known to possess better intuitive feelings or “hunch” compared to men. Despite these examples, such a precognitive interdiction is hardly accepted in established science. In this letter, we discuss briefly the advanced solutions of Maxwell equations, and then explore plausible connection between syntropy and precognition.

An Exploration Of The Effectiveness Of The Use Of Communication Apps Through Mobile Devices On Children With Autism Spectrum Disorders (Asd), Miriam O Sullivan

Theses

Autism Spectrum Disorder (ASD) is a complex neurological disorder which impacts on people in three primary areas: communication skills, social skills and behaviour skills. The diagnosis of ASD continues to rise with an estimated 1 in 100 receiving diagnoses of ASD in Ireland. There are many interventions promoted on a regular basis that claim positive effects on the basis of empirical research. However, the claims made by such studies are sometimes a little at odds with the level of methodological rigour and/or sample size employed. The research available is based on small scale international studies (America, Canada and Australia); however, …

Least-Squares Mapping From Kinematic Data To Acoustic Synthesis Parameters For Rehabilitative Acoustic Learning, Xiangyu Zhou

Master's Theses (2009 -)

Thousands of people suffer from dysarthria resulting from neurological injury of the motor component of the motor-speech system, and need to rely on alternative methods to communicate in daily life, such as body language or text-to-speech [1] . However, there are currently very few effective rehabilitative therapies for helping these patients improve their speech. Because of this, research is needed to develop better rehabilitative therapies. One such area of research is the use of involuntary acoustic learning. The Speech and Swallowing lab at Marquette University has an Electromagnetic Articulography (EMA) system to collect kinematic data and a software system called …

Towards An Automated Screening Tool For Pediatric Speech Delay, Roozbeh Sadeghian, Stephen A. Zahorian

Faculty Works

Speech delay is a childhood language problem that sometimes is resolved on its own but sometimes may cause more serious language difficulties later. This leads therapists to screen children for detection at early ages in order to eliminate future problems. Using the Goldman-Fristoe Test of Articulation (GFTA) method, therapists listen to a child's pronunciation of certain phonemes and phoneme pairs in specified words and judge the child's stage of speech development. The goal of this paper is to develop an Automatic Speech Recognition (ASR) tool and related speech processing methods which emulate the knowledge of speech therapists. In this paper …

Across-Speaker Articulatory Normalization For Speaker-Independent Silent Speech Recognition, Jun Wang, Ashok Samal, Jordan Green

School of Computing: Conference and Workshop Papers

Silent speech interfaces (SSIs), which recognize speech from articulatory information (i.e., without using audio information), have the potential to enable persons with laryngectomy or a neurological disease to produce synthesized speech with a natural sounding voice using their tongue and lips. Current approaches to SSIs have largely relied on speaker-dependent recognition models to minimize the negative effects of talker variation on recognition accuracy. Speaker-independent approaches are needed to reduce the large amount of training data required from each user; only limited articulatory samples are often available for persons with moderate to severe speech impairments, due to the logistic difficulty of …

Preliminary Test Of A Real-Time, Interactive Silent Speech Interface Based On Electromagnetic Articulograph, Jun Wang, Ashok Samal, Jordan R. Green

School of Computing: Conference and Workshop Papers

A silent speech interface (SSI) maps articulatory movement data to speech output. Although still in experimental stages, silent speech interfaces hold significant potential for facilitating oral communication in persons after laryngectomy or with other severe voice impairments. Despite the recent efforts on silent speech recognition algorithm development using offline data analysis, online test of SSIs have rarely been conducted. In this paper, we present a preliminary, online test of a real-time, interactive SSI based on electromagnetic motion tracking. The SSI played back synthesized speech sounds in response to the user’s tongue and lip movements. Three English talkers participated in this …

Articulatory Distinctiveness Of Vowels And Consonants: A Data-Driven Approach, Jun Wang, Jordan R. Green, Ashok Samal, Yana Yunusova

School of Computing: Faculty Publications

Purpose: To quantify the articulatory distinctiveness of 8 major English vowels and 11 English consonants based on tongue and lip movement time series data using a data-driven approach.

Method: Tongue and lip movements of 8 vowels and 11 consonants from 10 healthy talkers were collected. First, classification accuracies were obtained using 2 complementary approaches: (a) Procrustes analysis and (b) a support vector machine. Procrustes distance was then used to measure the articulatory distinctiveness among vowels and consonants. Finally, the distance (distinctiveness) matrices of different vowel pairs and consonant pairs were used to derive articulatory vowel and consonant spaces …

Word Recognition From Continuous Articulatory Movement Time-Series Data Using Symbolic Representations, Jun Wang, Arvind Balasubramanian, Luis Mojica De La Vega, Jordan R. Green, Ashok Samal, Balakrishnan Prabhakaran

School of Computing: Conference and Workshop Papers

Although still in experimental stage, articulation-based silent speech interfaces may have significant potential for facilitating oral communication in persons with voice and speech problems. An articulation-based silent speech interface converts articulatory movement information to audible words. The complexity of speech production mechanism (e.g., co-articulation) makes the conversion a formidable problem. In this paper, we reported a novel, real-time algorithm for recognizing words from continuous articulatory movements. This approach differed from prior work in that (1) it focused on word-level, rather than phoneme-level; (2) online segmentation and recognition were conducted at the same time; and (3) a symbolic representation (SAX) was …

Whole-Word Recognition From Articulatory Movements For Silent Speech Interfaces, Jun Wang, Ashok Samal, Jordan R. Green, Frank Rudzicz

Department of Special Education and Communication Disorders: Faculty Publications

Articulation-based silent speech interfaces convert silently produced speech movements into audible words. These systems are still in their experimental stages, but have significant potential for facilitating oral communication in persons with laryngectomy or speech impairments. In this paper, we report the result of a novel, real-time algorithm that recognizes whole-words based on articulatory movements. This approach differs from prior work that has focused primarily on phoneme-level recognition based on articulatory features. On average, our algorithm missed 1.93 words in a sequence of twenty-five words with an average latency of 0.79 seconds for each word prediction using a data set of …

Sentence Recognition From Articulatory Movements For Silent Speech Interfaces, Jun Wang, Ashok Samal, Jordan R. Green, Frank Rudzicz

Department of Special Education and Communication Disorders: Faculty Publications

Recent research has demonstrated the potential of using an articulation-based silent speech interface for command-and-control systems. Such an interface converts articulation to words that can then drive a text-to-speech synthesizer. In this paper, we have proposed a novel near-time algorithm to recognize whole-sentences from continuous tongue and lip movements. Our goal is to assist persons who are aphonic or have a severe motor speech impairment to produce functional speech using their tongue and lips. Our algorithm was tested using a functional sentence data set collected from ten speakers (3012 utterances). The average accuracy was 94.89% with an average latency of …

Bridging The Research Gap: Making Hri Useful To Individuals With Autism, Elizabeth Kim, Rhea Paul, Frederick Shic, Brian Scassellati

Communication Disorders Faculty Publications

While there is a rich history of studies involving robots and individuals with autism spectrum disorders (ASD), few of these studies have made substantial impact in the clinical research community. In this paper we first examine how differences in approach, study design, evaluation, and publication practices have hindered uptake of these research results. Based on ten years of collaboration, we suggest a set of design principles that satisfy the needs (both academic and cultural) of both the robotics and clinical autism research communities. Using these principles, we present a study that demonstrates a quantitatively measured improvement in human-human social interaction …

Vowel Recognition From Continuous Articulatory Movements For Speaker-Dependent Applications, Jun Wang, Jordan R. Green, Ashok Samal, Tom D. Carrell

Department of Special Education and Communication Disorders: Faculty Publications

A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels. This finding parallels previous empirical findings on tongue movements during vowels. The recognition algorithm was then used to drive an articulation-to-acoustics synthesizer. The synthesizer recognizes vowels from continuous input stream of tongue and lip movements and plays the corresponding sound samples …

Gaussian Mixture Models And Neural Networks For Automatic Speaker Identification, Usha Gayatri Chalkapally

Electrical & Computer Engineering Theses & Dissertations

Automatic Speaker Recognition is the process of automatically recognizing who is speaking on the basis of individual information contained in speech signals. This technique of Automatic Speaker Recognition makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.

In this thesis, the techniques of Gaussian Mixture Models and Neural Networks for Automatic Speaker Identification are presented. Algorithms for Speaker Identification using Gaussian Mixture Models were developed, …

Introduction To N-Adaptive Fuzzy Models To Analyze Public Opinion On Aids, Florentin Smarandache, W.B. Vasantha Kandasamy

Branch Mathematics and Statistics Faculty and Staff Publications

“AIDS is not simply a physical malady, it is also an artifact of social and sexual transgression, violated taboo, fractured identity—political and personal projections. Its key words are primarily the property of the powerful. AIDS: Keywords – is my attempt to identify and contest some of the assumptions underlying our current ‘knowledge’. In this effort I am joined by many AIDS activists including people living with AIDS— Acquired Immuno Deficiency Syndrome. “A syndrome is a pattern of symptoms pointing to a “morbid state” which may or may not be caused by infectious agents; a disease, on the other hand is, …

A Computer-Based Articulation Training Aid For Short Words (Cata), Mukund Devarajan

Electrical & Computer Engineering Theses & Dissertations

Several improvements in the vowel articulation training aid (VATA) are described, as well as the efforts to extend the visual feedback system to operate with short words in the form of consonant, vowel and consonant (CVC). The extended version of the visual feedback system is referred to as CATA (Computer-based Articulation Training Aid); the vowel version of the aid (VATA) only operates with ten American English monopthong vowels. Improvements in VATA include the use of a neural network (NN) recognizer method to prune a large database of vowel recordings to eliminate noisy and/or mispronounced tokens. The spectral jitter problem, previously …

Automatic Speaker Identification Using Reusable And Retrainable Binary-Pair Partitioned Neural Networks, Ashutosh Mishra

Electrical & Computer Engineering Theses & Dissertations

This thesis presents an extension of the work previously done on speaker identification using Binary Pair Partitioned (BPP) neural networks. In the previous work, a separate network was used for each pair of speakers in the speaker population. Although the basic BPP approach did perform well and had a simple underlying algorithm, it had the obvious disadvantage of requiring an extremely large number of networks for speaker identification with large speaker populations. It also requires training of networks proportional to the square of the number of speakers under consideration, leading to a very large number of networks to be trained …

Yet Another Algorithm For Pitch Tracking (Yaapt), Kavita Kasi

Electrical & Computer Engineering Theses & Dissertations

This thesis presents a pitch detection algorithm that is extremely robust for both high quality and telephone speech. The kernel method for this algorithm is the Normalized Cross Correlation (NCCF) reported by David Talkin [16]. Major innovations include: processing of the original acoustic signal and a nonlinearly processed version of the signal to partially restore very weak F0 components; intelligent peak picking to select multiple F0 candidates and assign merit factors; and, incorporation of highly robust pitch contours obtained from smoothed versions of low frequency portions of spectrograms. Dynamic programming is used to find the ''best" pitch track among all …

Minimum Mean Square Error Spectral Peak Envelope Estimation For Automatic Vowel Classification, Jaishree Venugopal

Electrical & Computer Engineering Theses & Dissertations

Spectral feature computations continue to be a very difficult problem for accurate machine recognition of speech. In this work, which focuses on vowels, a new spectral peak envelope method for vowel classification is developed, based on a missing frequency components model of speech recognition. According to the missing frequency components model, vowel recognition depends only on the spectral (harmonic) peaks. Smoothing and interpolation of the spectra, performed in the standard cepstral analysis method commonly used in automatic speech recognition, actually loses valuable information and results in reduced recognition accuracy. The new method for feature extraction presented in this thesis is …

Cerumen Composition By Flash Pyrolysis-Gas Chromatography/Mass Spectrometry, Craig N. Burkhart, Michael A. Kruge, Craig G. Burkhart, Curtis Black

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

Objective: To assess the chemical composition of cerumen by flash pyrolysis-gas chromatography/mass spectrometry.

Study Design: Collected earwax specimens were fractionated into residue and supernatant by means of deoxycholate. This natural bile acid produces significantly better disintegration of earwax in vitro than do presently available ceruminolytic preparations, and also has demonstrated excellent clinical results in vivo to date.

Patients: The sample for analysis was obtained from a patient with clinical earwax impaction.

Results: The supernatant is composed of simple aromatic hydrocarbons, C₅-C_{l 7} straight-chain hydrocarbons, a complex mixture of compounds tentatively identified as diterpenoids, …

Variability Analysis Of Discrete Cosine Transform Coefficient (Dctc) Features For Speech Processing, Bingjun Dai

Electrical & Computer Engineering Theses & Dissertations

In this research, the variability of Discrete Cosine Transform Coefficient (DCTC) features was investigated. Additionally, a new pitch-synchronous processing method was explored to increase the stability of features and to reduce window effects when compared to the regular method. The noise sources that lead to feature variability were analyzed, and different smoothing methods were tested. It was found that longer frames, frequency warping, time smoothing of the log spectrum, and DCS level time smoothing, all help reduce DCTC variability and increase classification performance. The pitch­ synchronous method was implemented with Matlab. Important processing methods, including pitch period estimation, time­ domain …

Text Independent Speaker Verification Using Binary-Pair Partitioned Neural Networks, Claude A. Norton Iii

Electrical & Computer Engineering Theses & Dissertations

A method is presented for the application of binary-pair partitioned neural networks to the task of speaker verification. This technique is based on a previously developed neural network classifier for speaker identification.

The main focus of this research was the development and testing of the algorithms necessary to extend the binary-pair partitioning approach from speaker identification to speaker verification. The method is based on the development of a user profile which is obtained from discriminative data provided by the binary-pair partitioned neural networks.

Experimental results are provided which demonstrate the viability of this approach, using the TIMIT speech corpus for …

Graduate Bulletin, 1995-1996 (1995), Moorhead State University

Graduate Bulletins (Catalogs)

No abstract provided.

Graduate Bulletin, 1993-1995, Moorhead State University

Graduate Bulletins (Catalogs)

No abstract provided.

Formant Estimation From Dctc's Using A Feedforward Neural Network, Shubhangi U. Kelkar

Electrical & Computer Engineering Theses & Dissertations

Formants are the natural frequencies of the human vocal tract. Existing methods for estimating formants from speech signals are computationally complex and subject to errors for certain type of speech sounds. This thesis describes a method for estimating vowel formant frequencies from Discrete Cosine Transform Coefficients (DCTC's), a form of cepstral coefficients, using a feedforward neural network with back-propagation training. Experimental results are based on a large multispeaker data base. The results are obtained for both a linear transformation and a feedforward neural network with a nonlinear hidden layer. In general, the neural network transformation is superior to the linear …