Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 3 of 3
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- All Subjects: Cochlear Implants
- All Subjects: DNN weight memory
Description
The present study describes audiovisual sentence recognition in normal hearing listeners, bimodal cochlear implant (CI) listeners and bilateral CI listeners. This study explores a new set of sentences (the AzAV sentences) that were created to have equal auditory intelligibility and equal gain from visual information.
The aims of Experiment I were to (i) compare the lip reading difficulty of the AzAV sentences to that of other sentence materials, (ii) compare the speech-reading ability of CI listeners to that of normal-hearing listeners and (iii) assess the gain in speech understanding when listeners have both auditory and visual information from easy-to-lip-read and difficult-to-lip read sentences. In addition, the sentence lists were subjected to a multi-level text analysis to determine the factors that make sentences easy or difficult to speech read.
The results of Experiment I showed that (i) the AzAV sentences were relatively difficult to lip read, (ii) that CI listeners and normal-hearing listeners did not differ in lip reading ability and (iii) that sentences with low lip-reading intelligibility (10-15 % correct) provide about a 30 percentage point improvement in speech understanding when added to the acoustic stimulus, while sentences with high lip-reading intelligibility (30-60 % correct) provide about a 50 percentage point improvement in the same comparison. The multi-level text analyses showed that the familiarity of phrases in the sentences was the primary driving factor that affects the lip reading difficulty.
The aim of Experiment II was to investigate the value, when visual information is present, of bimodal hearing and bilateral cochlear implants. The results of Experiment II showed that when visual information is present, low-frequency acoustic hearing can be of value to speech understanding for patients fit with a single CI. However, when visual information was available no gain was seen from the provision of a second CI, i.e., bilateral CIs. As was the case in Experiment I, visual information provided about a 30 percentage point improvement in speech understanding.
The aims of Experiment I were to (i) compare the lip reading difficulty of the AzAV sentences to that of other sentence materials, (ii) compare the speech-reading ability of CI listeners to that of normal-hearing listeners and (iii) assess the gain in speech understanding when listeners have both auditory and visual information from easy-to-lip-read and difficult-to-lip read sentences. In addition, the sentence lists were subjected to a multi-level text analysis to determine the factors that make sentences easy or difficult to speech read.
The results of Experiment I showed that (i) the AzAV sentences were relatively difficult to lip read, (ii) that CI listeners and normal-hearing listeners did not differ in lip reading ability and (iii) that sentences with low lip-reading intelligibility (10-15 % correct) provide about a 30 percentage point improvement in speech understanding when added to the acoustic stimulus, while sentences with high lip-reading intelligibility (30-60 % correct) provide about a 50 percentage point improvement in the same comparison. The multi-level text analyses showed that the familiarity of phrases in the sentences was the primary driving factor that affects the lip reading difficulty.
The aim of Experiment II was to investigate the value, when visual information is present, of bimodal hearing and bilateral cochlear implants. The results of Experiment II showed that when visual information is present, low-frequency acoustic hearing can be of value to speech understanding for patients fit with a single CI. However, when visual information was available no gain was seen from the provision of a second CI, i.e., bilateral CIs. As was the case in Experiment I, visual information provided about a 30 percentage point improvement in speech understanding.
ContributorsWang, Shuai (Author) / Dorman, Michael (Thesis advisor) / Berisha, Visar (Committee member) / Liss, Julie (Committee member) / Arizona State University (Publisher)
Created2015
Description
Through decades of clinical progress, cochlear implants have brought the world of speech and language to thousands of profoundly deaf patients. However, the technology has many possible areas for improvement, including providing information of non-linguistic cues, also called indexical properties of speech. The field of sensory substitution, providing information relating one sense to another, offers a potential avenue to further assist those with cochlear implants, in addition to the promise they hold for those without existing aids. A user study with a vibrotactile device is evaluated to exhibit the effectiveness of this approach in an auditory gender discrimination task. Additionally, preliminary computational work is included that demonstrates advantages and limitations encountered when expanding the complexity of future implementations.
ContributorsButts, Austin McRae (Author) / Helms Tillery, Stephen (Thesis advisor) / Berisha, Visar (Committee member) / Buneo, Christopher (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2015
Description
Deep neural networks (DNN) have shown tremendous success in various cognitive tasks, such as image classification, speech recognition, etc. However, their usage on resource-constrained edge devices has been limited due to high computation and large memory requirement.
To overcome these challenges, recent works have extensively investigated model compression techniques such as element-wise sparsity, structured sparsity and quantization. While most of these works have applied these compression techniques in isolation, there have been very few studies on application of quantization and structured sparsity together on a DNN model.
This thesis co-optimizes structured sparsity and quantization constraints on DNN models during training. Specifically, it obtains optimal setting of 2-bit weight and 2-bit activation coupled with 4X structured compression by performing combined exploration of quantization and structured compression settings. The optimal DNN model achieves 50X weight memory reduction compared to floating-point uncompressed DNN. This memory saving is significant since applying only structured sparsity constraints achieves 2X memory savings and only quantization constraints achieves 16X memory savings. The algorithm has been validated on both high and low capacity DNNs and on wide-sparse and deep-sparse DNN models. Experiments demonstrated that deep-sparse DNN outperforms shallow-dense DNN with varying level of memory savings depending on DNN precision and sparsity levels. This work further proposed a Pareto-optimal approach to systematically extract optimal DNN models from a huge set of sparse and dense DNN models. The resulting 11 optimal designs were further evaluated by considering overall DNN memory which includes activation memory and weight memory. It was found that there is only a small change in the memory footprint of the optimal designs corresponding to the low sparsity DNNs. However, activation memory cannot be ignored for high sparsity DNNs.
To overcome these challenges, recent works have extensively investigated model compression techniques such as element-wise sparsity, structured sparsity and quantization. While most of these works have applied these compression techniques in isolation, there have been very few studies on application of quantization and structured sparsity together on a DNN model.
This thesis co-optimizes structured sparsity and quantization constraints on DNN models during training. Specifically, it obtains optimal setting of 2-bit weight and 2-bit activation coupled with 4X structured compression by performing combined exploration of quantization and structured compression settings. The optimal DNN model achieves 50X weight memory reduction compared to floating-point uncompressed DNN. This memory saving is significant since applying only structured sparsity constraints achieves 2X memory savings and only quantization constraints achieves 16X memory savings. The algorithm has been validated on both high and low capacity DNNs and on wide-sparse and deep-sparse DNN models. Experiments demonstrated that deep-sparse DNN outperforms shallow-dense DNN with varying level of memory savings depending on DNN precision and sparsity levels. This work further proposed a Pareto-optimal approach to systematically extract optimal DNN models from a huge set of sparse and dense DNN models. The resulting 11 optimal designs were further evaluated by considering overall DNN memory which includes activation memory and weight memory. It was found that there is only a small change in the memory footprint of the optimal designs corresponding to the low sparsity DNNs. However, activation memory cannot be ignored for high sparsity DNNs.
ContributorsSrivastava, Gaurav (Author) / Seo, Jae-Sun (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2018