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Description
Previous studies have found that the detection of near-threshold stimuli is decreased immediately before movement and throughout movement production. This has been suggested to occur through the use of the internal forward model processing an efferent copy of the motor command and creating a prediction that is used to cancel out the resulting sensory feedback. Currently, there are no published accounts of the perception of tactile signals for motor tasks and contexts related to the lips during both speech planning and production. In this study, we measured the responsiveness of the somatosensory system during speech planning using light electrical stimulation below the lower lip by comparing perception during mixed speaking and silent reading conditions. Participants were asked to judge whether a constant near-threshold electrical stimulation (subject-specific intensity, 85% detected at rest) was present during different time points relative to an initial visual cue. In the speaking condition, participants overtly produced target words shown on a computer monitor. In the reading condition, participants read the same target words silently to themselves without any movement or sound. We found that detection of the stimulus was attenuated during speaking conditions while remaining at a constant level close to the perceptual threshold throughout the silent reading condition. Perceptual modulation was most intense during speech production and showed some attenuation just prior to speech production during the planning period of speech. This demonstrates that there is a significant decrease in the responsiveness of the somatosensory system during speech production as well as milliseconds before speech is even produced which has implications for speech disorders such as stuttering and schizophrenia with pronounced deficits in the somatosensory system.
ContributorsMcguffin, Brianna Jean (Author) / Daliri, Ayoub (Thesis director) / Liss, Julie (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Previous research has shown that a loud acoustic stimulus can trigger an individual's prepared movement plan. This movement response is referred to as a startle-evoked movement (SEM). SEM has been observed in the stroke survivor population where results have shown that SEM enhances single joint movements that are usually performed with difficulty. While the presence of SEM in the stroke survivor population advances scientific understanding of movement capabilities following a stroke, published studies using the SEM phenomenon only examined one joint. The ability of SEM to generate multi-jointed movements is understudied and consequently limits SEM as a potential therapy tool. In order to apply SEM as a therapy tool however, the biomechanics of the arm in multi-jointed movement planning and execution must be better understood. Thus, the objective of our study was to evaluate if SEM could elicit multi-joint reaching movements that were accurate in an unrestrained, two-dimensional workspace. Data was collected from ten subjects with no previous neck, arm, or brain injury. Each subject performed a reaching task to five Targets that were equally spaced in a semi-circle to create a two-dimensional workspace. The subject reached to each Target following a sequence of two non-startling acoustic stimuli cues: "Get Ready" and "Go". A loud acoustic stimuli was randomly substituted for the "Go" cue. We hypothesized that SEM is accessible and accurate for unrestricted multi-jointed reaching tasks in a functional workspace and is therefore independent of movement direction. Our results found that SEM is possible in all five Target directions. The probability of evoking SEM and the movement kinematics (i.e. total movement time, linear deviation, average velocity) to each Target are not statistically different. Thus, we conclude that SEM is possible in a functional workspace and is not dependent on where arm stability is maximized. Moreover, coordinated preparation and storage of a multi-jointed movement is indeed possible.
ContributorsOssanna, Meilin Ryan (Author) / Honeycutt, Claire (Thesis director) / Schaefer, Sydney (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Speech intelligibility measures how much a speaker can be understood by a listener. Traditional measures of intelligibility, such as word accuracy, are not sufficient to reveal the reasons of intelligibility degradation. This dissertation investigates the underlying sources of intelligibility degradations from both perspectives of the speaker and the listener. Segmental phoneme errors and suprasegmental lexical boundary errors are developed to reveal the perceptual strategies of the listener. A comprehensive set of automated acoustic measures are developed to quantify variations in the acoustic signal from three perceptual aspects, including articulation, prosody, and vocal quality. The developed measures have been validated on a dysarthric speech dataset with various severity degrees. Multiple regression analysis is employed to show the developed measures could predict perceptual ratings reliably. The relationship between the acoustic measures and the listening errors is investigated to show the interaction between speech production and perception. The hypothesize is that the segmental phoneme errors are mainly caused by the imprecise articulation, while the sprasegmental lexical boundary errors are due to the unreliable phonemic information as well as the abnormal rhythm and prosody patterns. To test the hypothesis, within-speaker variations are simulated in different speaking modes. Significant changes have been detected in both the acoustic signals and the listening errors. Results of the regression analysis support the hypothesis by showing that changes in the articulation-related acoustic features are important in predicting changes in listening phoneme errors, while changes in both of the articulation- and prosody-related features are important in predicting changes in lexical boundary errors. Moreover, significant correlation has been achieved in the cross-validation experiment, which indicates that it is possible to predict intelligibility variations from acoustic signal.
ContributorsJiao, Yishan (Author) / Berisha, Visar (Thesis advisor) / Liss, Julie (Thesis advisor) / Zhou, Yi (Committee member) / Arizona State University (Publisher)
Created2019
Description
In many biological research studies, including speech analysis, clinical research, and prediction studies, the validity of the study is dependent on the effectiveness of the training data set to represent the target population. For example, in speech analysis, if one is performing emotion classification based on speech, the performance of the classifier is mainly dependent on the number and quality of the training data set. For small sample sizes and unbalanced data, classifiers developed in this context may be focusing on the differences in the training data set rather than emotion (e.g., focusing on gender, age, and dialect).
This thesis evaluates several sampling methods and a non-parametric approach to sample sizes required to minimize the effect of these nuisance variables on classification performance. This work specifically focused on speech analysis applications, and hence the work was done with speech features like Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Cepstral Coefficients (FBCC). The non-parametric divergence (D_p divergence) measure was used to study the difference between different sampling schemes (Stratified and Multistage sampling) and the changes due to the sentence types in the sampling set for the process.
This thesis evaluates several sampling methods and a non-parametric approach to sample sizes required to minimize the effect of these nuisance variables on classification performance. This work specifically focused on speech analysis applications, and hence the work was done with speech features like Mel-Frequency Cepstral Coefficients (MFCC) and Filter Bank Cepstral Coefficients (FBCC). The non-parametric divergence (D_p divergence) measure was used to study the difference between different sampling schemes (Stratified and Multistage sampling) and the changes due to the sentence types in the sampling set for the process.
ContributorsMariajohn, Aaquila (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2020
Description
Speech analysis for clinical applications has emerged as a burgeoning field, providing valuable insights into an individual's physical and physiological state. Researchers have explored speech features for clinical applications, such as diagnosing, predicting, and monitoring various pathologies. Before presenting the new deep learning frameworks, this thesis introduces a study on conventional acoustic feature changes in subjects with post-traumatic headache (PTH) attributed to mild traumatic brain injury (mTBI). This work demonstrates the effectiveness of using speech signals to assess the pathological status of individuals. At the same time, it highlights some of the limitations of conventional acoustic and linguistic features, such as low repeatability and generalizability. Two critical characteristics of speech features are (1) good robustness, as speech features need to generalize across different corpora, and (2) high repeatability, as speech features need to be invariant to all confounding factors except the pathological state of targets. This thesis presents two research thrusts in the context of speech signals in clinical applications that focus on improving the robustness and repeatability of speech features, respectively. The first thrust introduces a deep learning framework to generate acoustic feature embeddings sensitive to vocal quality and robust across different corpora. A contrastive loss combined with a classification loss is used to train the model jointly, and data-warping techniques are employed to improve the robustness of embeddings. Empirical results demonstrate that the proposed method achieves high in-corpus and cross-corpus classification accuracy and generates good embeddings sensitive to voice quality and robust across different corpora. The second thrust introduces using the intra-class correlation coefficient (ICC) to evaluate the repeatability of embeddings. A novel regularizer, the ICC regularizer, is proposed to regularize deep neural networks to produce embeddings with higher repeatability. This ICC regularizer is implemented and applied to three speech applications: a clinical application, speaker verification, and voice style conversion. The experimental results reveal that the ICC regularizer improves the repeatability of learned embeddings compared to the contrastive loss, leading to enhanced performance in downstream tasks.
ContributorsZhang, Jianwei (Author) / Jayasuriya, Suren (Thesis advisor) / Berisha, Visar (Thesis advisor) / Liss, Julie (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2023