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- All Subjects: Speech Perception
- All Subjects: Primary Motor Cortex
- Creators: Arunachalam, Sairam
- Creators: Barragan, Beatriz
- Member of: Theses and Dissertations
Description
The activation of the primary motor cortex (M1) is common in speech perception tasks that involve difficult listening conditions. Although the challenge of recognizing and discriminating non-native speech sounds appears to be an instantiation of listening under difficult circumstances, it is still unknown if M1 recruitment is facilitatory of second language speech perception. The purpose of this study was to investigate the role of M1 associated with speech motor centers in processing acoustic inputs in the native (L1) and second language (L2), using repetitive Transcranial Magnetic Stimulation (rTMS) to selectively alter neural activity in M1. Thirty-six healthy English/Spanish bilingual subjects participated in the experiment. The performance on a listening word-to-picture matching task was measured before and after real- and sham-rTMS to the orbicularis oris (lip muscle) associated M1. Vowel Space Area (VSA) obtained from recordings of participants reading a passage in L2 before and after real-rTMS, was calculated to determine its utility as an rTMS aftereffect measure. There was high variability in the aftereffect of the rTMS protocol to the lip muscle among the participants. Approximately 50% of participants showed an inhibitory effect of rTMS, evidenced by smaller motor evoked potentials (MEPs) area, whereas the other 50% had a facilitatory effect, with larger MEPs. This suggests that rTMS has a complex influence on M1 excitability, and relying on grand-average results can obscure important individual differences in rTMS physiological and functional outcomes. Evidence of motor support to word recognition in the L2 was found. Participants showing an inhibitory aftereffect of rTMS on M1 produced slower and less accurate responses in the L2 task, whereas those showing a facilitatory aftereffect of rTMS on M1 produced more accurate responses in L2. In contrast, no effect of rTMS was found on the L1, where accuracy and speed were very similar after sham- and real-rTMS. The L2 VSA measure was indicative of the aftereffect of rTMS to M1 associated with speech production, supporting its utility as an rTMS aftereffect measure. This result revealed an interesting and novel relation between cerebral motor cortex activation and speech measures.
ContributorsBarragan, Beatriz (Author) / Liss, Julie (Thesis advisor) / Berisha, Visar (Committee member) / Rogalsky, Corianne (Committee member) / Restrepo, Adelaida (Committee member) / Arizona State University (Publisher)
Created2018
Description
Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. While deep neural network (DNN) implementation of these systems have very good performance,
they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost
of keyword detection and speech recognition networks (or DNNs) are presented.
The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the
accuracy. Experiments conducted on the Resource Management (RM) database show that for the keyword detection neural network, representing the weights by 5 bits results in a 6 fold reduction in memory compared to a floating point implementation with very little loss in performance. Similarly, for the speech recognition neural network, representing the weights by 6 bits results in a 5 fold reduction in memory while maintaining an error rate similar to a floating point implementation. Additional reduction in memory is achieved by a technique called weight pruning,
where the weights are classified as sensitive and insensitive and the sensitive weights are represented with higher precision. A combination of these two techniques helps reduce the memory
footprint by 81 - 84% for speech recognition and keyword detection networks respectively.
Further reduction in memory size is achieved by judiciously dropping connections for large blocks of weights. The corresponding technique, termed coarse-grain sparsification, introduces
hardware-aware sparsity during DNN training, which leads to efficient weight memory compression and significant reduction in the number of computations during classification without
loss of accuracy. Keyword detection and speech recognition DNNs trained with 75% of the weights dropped and classified with 5-6 bit weight precision effectively reduced the weight memory
requirement by ~95% compared to a fully-connected network with double precision, while showing similar performance in keyword detection accuracy and word error rate.
they have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this thesis, techniques to reduce the memory and computation cost
of keyword detection and speech recognition networks (or DNNs) are presented.
The first technique is based on representing all weights and biases by a small number of bits and mapping all nodal computations into fixed-point ones with minimal degradation in the
accuracy. Experiments conducted on the Resource Management (RM) database show that for the keyword detection neural network, representing the weights by 5 bits results in a 6 fold reduction in memory compared to a floating point implementation with very little loss in performance. Similarly, for the speech recognition neural network, representing the weights by 6 bits results in a 5 fold reduction in memory while maintaining an error rate similar to a floating point implementation. Additional reduction in memory is achieved by a technique called weight pruning,
where the weights are classified as sensitive and insensitive and the sensitive weights are represented with higher precision. A combination of these two techniques helps reduce the memory
footprint by 81 - 84% for speech recognition and keyword detection networks respectively.
Further reduction in memory size is achieved by judiciously dropping connections for large blocks of weights. The corresponding technique, termed coarse-grain sparsification, introduces
hardware-aware sparsity during DNN training, which leads to efficient weight memory compression and significant reduction in the number of computations during classification without
loss of accuracy. Keyword detection and speech recognition DNNs trained with 75% of the weights dropped and classified with 5-6 bit weight precision effectively reduced the weight memory
requirement by ~95% compared to a fully-connected network with double precision, while showing similar performance in keyword detection accuracy and word error rate.
ContributorsArunachalam, Sairam (Author) / Chakrabarti, Chaitali (Thesis advisor) / Seo, Jae-Sun (Thesis advisor) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2016