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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

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
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Description
Cochlear implant (CI) successfully restores hearing sensation to profoundly deaf patients, but its
performance is limited by poor spectral resolution. Acoustic CI simulation has been widely used
in normal-­hearing (NH) listeners to study the effect of spectral resolution on speech perception,
while avoiding patient-­related confounds. It is unclear how speech production may change

Cochlear implant (CI) successfully restores hearing sensation to profoundly deaf patients, but its
performance is limited by poor spectral resolution. Acoustic CI simulation has been widely used
in normal-­hearing (NH) listeners to study the effect of spectral resolution on speech perception,
while avoiding patient-­related confounds. It is unclear how speech production may change with
the degree of spectral degradation of auditory feedback as experience by CI users. In this study,
a real-­time sinewave CI simulation was developed to provide NH subjects with auditory
feedback of different spectral resolution (1, 2, 4, and 8 channels). NH subjects were asked to
produce and identify vowels, as well as recognize sentences while listening to the real-­time CI
simulation. The results showed that sentence recognition scores with the real-­time CI simulation
improved with more channels, similar to those with the traditional off-­line CI simulation.
Perception of a vowel continuum “HEAD”-­ “HAD” was near chance with 1, 2, and 4 channels,
and greatly improved with 8 channels and full spectrum. The spectral resolution of auditory
feedback did not significantly affect any acoustic feature of vowel production (e.g., vowel space
area, mean amplitude, mean and variability of fundamental and formant frequencies). There
was no correlation between vowel production and perception. The lack of effect of auditory
feedback spectral resolution on vowel production was likely due to the limited exposure of NH
subjects to CI simulation and the limited frequency ranges covered by the sinewave carriers of
CI simulation. Future studies should investigate the effects of various CI processing parameters
on speech production using a noise-­band CI simulation.
ContributorsPerez Lustre, Sarahi (Author) / Luo, Xin (Thesis director) / Daliri, Ayoub (Committee member) / Division of Teacher Preparation (Contributor) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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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

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.
ContributorsMariajohn, Aaquila (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2020
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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

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
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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

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