@article{nokey,

title = {Improving mispronunciation detection using speech reconstruction},

author = {A. Das and R. Gutierrez-Osuna,},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2024/07/anurag-taslp-2024.pdf},

year  = {2024},

date = {2024-07-19},

journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},

keywords = {Accent conversion, Speech},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{darpit2022jdst,

title = {Detection of hypoglycemia and hyperglycemia using ECG and accelerometer based non-invasive wearable sensors},

author = {D. Dave and K. Vyas and K. Branan and S. McKay and D. J. DeSalvo and R. Gutierrez-Osuna and G. L. Cote and M. Erraguntla},

url = {https://journals.sagepub.com/doi/full/10.1177/19322968221116393

https://psi.engr.tamu.edu/wp-content/uploads/2024/07/darpit-2024-jdst-1.pdf},

year  = {2024},

date = {2024-07-16},

urldate = {2024-07-16},

journal = {Journal of Diabetes Science and Technology},

volume = {18},

issue = {2},

keywords = {Continuous glucose monitors, Diabetes, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Does gamified breath-biofeedback promote adherence, relaxation, and skill transfer in the wild?},

author = {D. R. da C. Silva and R. Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2024/07/Dennis-TAC-GBF-2024.pdf},

year  = {2024},

date = {2024-07-15},

journal = {IEEE Transactions on Affective Computing},

keywords = {Games, Stress},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Corrective feedback accuracy and pronunciation improvement: Feedback that is ‘good enough’},

author = {A. Silpachai, R. Neiriz, M. Novotny, R. Gutierrez-Osuna, J. M. Levis and E. Chukharey

},

url = {https://hdl.handle.net/10125/73582https://psi.engr.tamu.edu/wp-content/uploads/2024/07/llt-2024-good-enough.pdf

https://hdl.handle.net/10125/73582},

year  = {2024},

date = {2024-06-17},

journal = {Language Learning & Technology},

volume = {28},

issue = {1},

pages = {1–16},

keywords = {Speech},

pubstate = {published},

tppubtype = {article}

}

@article{dennis2021tac,

title = {Towards Participant-Independent Stress Detection Using Instrumented Peripherals},

author = {D. R. da C. Silva and Z. Wang and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/9361293

https://psi.engr.tamu.edu/wp-content/uploads/2023/11/keyboard2023dennis.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2021-02-05},

journal = {IEEE Transactions on Affective Computing},

keywords = {Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {A review of digital innovations for diet monitoring and precision nutrition},

author = {B. J. Mortazavi and R. Gutierrez-Osuna},

url = {https://journals.sagepub.com/doi/full/10.1177/19322968211041356

https://psi.engr.tamu.edu/wp-content/uploads/2023/11/digitalInnovations2023bobak.pdf},

year  = {2023},

date = {2023-01-01},

urldate = {2021-07-14},

journal = {Journal of Diabetes Science and Technology},

keywords = {Chemical sensors, Diabetes, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{diana2022mesoporous,

title = {Silane-modified mesoporous silica nanocoatings for selective mid-infrared waveguide-based gas sensing},

author = {D. Al Husseini and P. T. Lin and S. A. Sukhishvili and J. Zhou and J. Li and P. T. Lin and R. Gutierrez-Osuna and G. L. Coté},

year  = {2022},

date = {2022-09-29},

journal = {Advanced Materials Interfaces},

keywords = {Chemical sensors},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{junchao2022ac,

title = {Mid-Infrared Serial Microring Resonator Array for Real-time Detection of Vapor Phase Volatile Organic Compounds},

author = {J. Zhou and D. Al Husseini and J. Li and Z. Lin and S. Sukhishvili and G. L. Cote and R. Gutierrez-Osuna and P. T. Lin },

year  = {2022},

date = {2022-07-19},

journal = {Analytical Chemistry},

keywords = {Chemical sensors, Infrared spectroscopy},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{anurag2021aminoacids,

title = {Predicting the macronutrient composition of mixed meals from dietary biomarkers in blood},

author = {A. Das and B. Mortazavi and S. Sajjadi and T. Chaspari and L. E. Ruebush and N. E. Deutz and G. L. Cote and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/9645322

https://psi.engr.tamu.edu/wp-content/uploads/2021/12/jbhi-aminoacids-2021-preprint.pdf},

year  = {2022},

date = {2022-06-04},

urldate = {2021-12-05},

journal = {IEEE Journal of Biomedical and Health Informatics},

volume = {26},

number = {6},

pages = {2726-2736},

keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{sudip-2022sab,

title = {On-line drift-compensation for continuous monitoring  with arrays of cross-sensitive chemical sensors},

author = {Sudip Paul and Rohit Sharma and Prashant Tathireddy and Ricardo Gutierrez-Osuna},

year  = {2022},

date = {2022-05-17},

journal = {Sensors and Actuators B: Chemical},

keywords = {Chemical sensors},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{junchao-2022-scirep,

title = {Detection of Volatile Organic Compounds Using Mid-Infrared Silicon Nitride Waveguide Sensors},

author = {Junchao Zhou and Diana Al Husseini and Junyan Li and Zhihai Lin and Svetlana Sukhishvili and Gerard L. Coté and Ricardo Gutierrez-Osuna and Pao Tai Lin},

url = {https://www.nature.com/articles/s41598-022-09597-9 

https://psi.engr.tamu.edu/wp-content/uploads/2022/04/s41598-022-09597-9.pdf},

year  = {2022},

date = {2022-03-15},

urldate = {2022-03-15},

journal = {Scientific Reports},

volume = { 12},

number = {5572 },

keywords = {Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@article{nitin2021digitalhealth,

title = {Evaluating the role of breathing guidance on game-based interventions for relaxation training},

author = {N. V. Chakravarthy and D. R. Da Cunha Silva and R. Gutierrez-Osuna},

url = {https://www.frontiersin.org/articles/10.3389/fdgth.2021.760268/abstract

https://psi.engr.tamu.edu/wp-content/uploads/2021/12/nitin2021frontiers.pdf},

year  = {2021},

date = {2021-11-18},

urldate = {2021-11-18},

journal = {Frontiers in Digital Health},

keywords = {Games, Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{shaojin2021accentron,

title = {Accentron: Foreign accent conversion to arbitrary non-native speakers using zero-shot learning},

author = {S. Ding and G. Zhao and R. Gutierrez-Osuna},

url = {https://www.sciencedirect.com/science/article/pii/S0885230821001029

https://psi.engr.tamu.edu/wp-content/uploads/2021/10/1-s2.0-S0885230821001029-main.pdf},

year  = {2021},

date = {2021-10-14},

urldate = {2021-10-14},

journal = {Computer Speech & Language},

keywords = {Accent conversion, Deep learning, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{martin2021clinicalnutrition,

title = {Postprandial concentration of circulating branched chain amino acids are able to predict the carbohydrate content of the ingested mixed meal},

author = {M. Hagve and S. Y. Simbo and L. E. Ruebush and M. P. K. J. Engelen and R. Gutierrez-Osuna and B. J. Mortazavi and G. L. Cote and N. E. P. Deutz},

url = {https://www.sciencedirect.com/science/article/pii/S0261561421003502

https://psi.engr.tamu.edu/wp-content/uploads/2021/07/1-s2.0-S0261561421003502-main.pdf},

year  = {2021},

date = {2021-07-13},

journal = {Clinical Nutrition},

keywords = {Chemical sensors, Continuous glucose monitors, Health, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{guanlong2021reference-free,

title = {Converting Foreign Accent Speech Without a Reference},

author = {G. Zhao and S. Ding and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/abstract/document/9477581

https://psi.engr.tamu.edu/wp-content/uploads/2021/08/zhao2021reference.pdf},

year  = {2021},

date = {2021-07-01},

urldate = {2021-07-01},

journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},

volume = {29},

pages = {2367},

keywords = {Accent conversion, Deep learning, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{adam2021TACCESS,

title = {A Longitudinal Evaluation of Tablet-Based Child Speech Therapy with Apraxia World},

author = {A. Hair and K. J. Ballard and C. Markoulli and P. Monroe and J. McKechnie and B. Ahmed and R. Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2021/08/hair_taccess_2021.pdf},

doi = {10.1145/3433607},

year  = {2021},

date = {2021-03-15},

journal = {ACM Transactions On Accessible Computing},

volume = {14},

number = {1},

abstract = {Digital games can make speech therapy exercises more enjoyable for children and increase their motivation during therapy. However, many such games developed to date have not been designed for long-term use. To address this issue, we developed Apraxia World, a speech therapy game specifically intended to be played over extended periods. In this study, we examined pronunciation improvements, child engagement over time, and caregiver and automated pronunciation evaluation accuracy while using our game over a multi-month period. Ten children played Apraxia World at home during two counterbalanced four-week treatment blocks separated by a two-week break. In one treatment phase, children received pronunciation feedback from caregivers and in the other treatment phase, utterances were evaluated with an automated framework built into the game. We found that children made therapeutically significant speech improvements while using Apraxia World, and that the game successfully increased engagement during speech therapy practice. Additionally, in offline mispronunciation detection tests, our automated pronunciation evaluation framework outperformed a traditional method based on goodness of pronunciation scoring. Our results suggest that this type of speech therapy game is a valid complement to traditional home practice.},

keywords = {Games, Health, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{parnandi2018taffc,

title = {Partial reinforcement in game biofeedback for relaxation training},

author = {A. Parnandi and R. Gutierrez-Osuna },

url = {https://ieeexplore.ieee.org/document/8400398

https://psi.engr.tamu.edu/wp-content/uploads/2021/02/08400398.pdf},

year  = {2021},

date = {2021-02-28},

journal = {IEEE Transactions on Affective Computing},

volume = {12},

number = {1},

pages = {141-153},

keywords = {Electrodermal activity, Games, Health, Heart rate variability, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{alhusseini2021coatings,

title = {Surface Functionalization Utilizing Mesoporous Silica Nanoparticles for Enhanced Evanescent-Field Mid-Infrared Waveguide Gas Sensing},

author = {D. Al Husseini and Y. Karanth and J. Zhou and D. Willhelm and X. Qian and R. Gutierrez-Osuna and G. L. Coté and and P. Tai Lin and S. A. Sukhishvili},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2021/01/ahhusseini2021coatings.pdf

https://www.mdpi.com/2079-6412/11/2/118},

year  = {2021},

date = {2021-01-21},

journal = {Coatings},

volume = {11},

number = {118},

pages = {1-12},

keywords = {Chemical sensors, Infrared spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{ahmed2018tac,

title = {Gaming away stress: Using biofeedback games to learn paced breathing},

author = {B Ahmed and M Zafar and R Rihawi and R Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/8319498},

year  = {2020},

date = {2020-12-02},

journal = {IEEE Transactions on Affective Computing},

volume = {11},

number = {3},

pages = {519-531},

keywords = {Electrodermal activity, Games, Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{zaman2019scientificdata,

title = {Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities},

author = {S. Zaman and A. Wesley and D. Rodrigo Da Cunha Silva and P. Buddharaju and F. Akbar and G. Gao and G. Mark and R. Gutierrez-Osuna and I. Pavlidis},

url = {https://www.nature.com/articles/s41597-019-0249-5

https://psi.engr.tamu.edu/wp-content/uploads/2020/04/shaila2019scientificdata.pdf},

year  = {2020},

date = {2020-11-08},

journal = {Scientific Data},

volume = {6},

number = {1},

keywords = {Electrodermal activity, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{alhusseini2020chemComm,

title = {All-Nanoparticle Layer-by-layer Coatings for Mid-IR On-Chip Gas Sensing},

author = {D. Al Husseini and J. Zhou and D. Willhelm and T. Hastings and G. S. Day and H.-C. Zhou and G. L. Coté and X. Qian and R. Gutierrez-Osuna, P. T. Lin and S. Sukhishvili},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2020/10/d0cc05513a.pdf

https://pubs.rsc.org/en/content/articlelanding/2020/cc/d0cc05513a#!divAbstract},

year  = {2020},

date = {2020-10-22},

journal = {Chemical Communications},

keywords = {Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@article{ivana2020ltr,

title = {The English Pronunciation of Arabic Speakers - A Data-Driven Approach to Segmental Error Identification},

author = {I. Lučić and A. Silpachai and J. Levis and G Zhao and R. Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2020/06/1362168820931888.pdf},

year  = {2020},

date = {2020-06-18},

journal = {Language Teaching Research},

keywords = {Accent conversion, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{McKechnie2020tabbyTalks,

title = {The influence of type of feedback during tablet-based delivery of intensive treatment for childhood apraxia of speech},

author = {J. McKechnie and B. Ahmed and R. Gutierrez-Osuna and E. Murray and P. McCabe and K. Ballard},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2020/08/1-s2.0-S0021992420300940-main.pdf

https://www.sciencedirect.com/science/article/pii/S0021992420300940},

year  = {2020},

date = {2020-05-20},

journal = {Journal of Communication Disorders},

keywords = {Health, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{shaojin-2019-taslp,

title = {Learning Structured Sparse Representations for Voice Conversion},

author = {S. Ding and G Zhao and C. Liberatore and R. Gutierrez-Osuna},

url = {https://ieeexplore.ieee.org/document/8910392

https://psi.engr.tamu.edu/wp-content/uploads/2020/04/shaojin2019taslp.pdf},

doi = {10.1109/TASLP.2019.2955289},

year  = {2019},

date = {2019-11-15},

journal = {IEEE Transactions on Audio, Speech and Language Processing},

volume = {28},

pages = {343-354},

keywords = {Accent conversion, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{shaojin-2019-speechcomm,

title = {Golden speaker builder – An interactive tool for pronunciation training},

author = {S. Ding and C. Liberatore and S. Sonsaat and I. Lučić and A. Silpachai and G Zhao and E. Chukharev-Hudilainen and J. Levis and R. Gutierrez-Osuna},

url = {https://www.sciencedirect.com/science/article/pii/S0167639319302675

https://psi.engr.tamu.edu/wp-content/uploads/2019/11/1-s2.0-S0167639319302675-main.pdf

},

year  = {2019},

date = {2019-11-14},

journal = {Speech Communication},

volume = {115},

pages = {51-66},

keywords = {Accent conversion, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{akbar2019sensors,

title = {An Empirical Study Comparing Unobtrusive Physiological Sensors for Stress Detection in Computer Work},

author = {F. Akbar and G. Mark and I. Pavlidis and R. Gutierrez-Osuna},

url = {https://www.mdpi.com/1424-8220/19/17/3766

https://psi.engr.tamu.edu/wp-content/uploads/2020/04/akbar2019sensors.pdf},

year  = {2019},

date = {2019-08-30},

journal = {Sensors},

volume = {19},

keywords = {Electrodermal activity, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{zhao-2019-taslp,

title = {Using Phonetic Posteriorgram Based Frame Pairing for Segmental Accent Conversion},

author = {G Zhao and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2020/04/zhao2019taslp.pdf},

doi = {10.1109/TASLP.2019.2926754},

issn = {2329-9290},

year  = {2019},

date = {2019-07-04},

urldate = {2019-07-04},

journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},

volume = {27},

number = {10},

pages = {1649-1660},

abstract = {Accent conversion (AC) aims to transform non-native utterances to sound as if the speaker had a native accent. This can be achieved by mapping source speech spectra from a native speaker into the acoustic space of the target non-native speaker. In prior work, we proposed an AC approach that matches frames between the two speakers based on their acoustic similarity after compensating for differences in vocal tract length. In this paper, we propose a new approach that matches frames between the two speakers based on their phonetic (rather than acoustic) similarity. Namely, we map frames from the two speakers into a phonetic posteriorgram using speaker-independent acoustic models trained on native speech. We thoroughly evaluate the approach on a speech corpus containing multiple native and non-native speakers. The proposed algorithm outperforms the prior approach, improving ratings of acoustic quality (22% increase in mean opinion score) and native accent (69% preference) while retaining the voice quality of the non-native speaker. Furthermore, we show that the approach can be used in the reverse conversion direction, i.e., generating speech with a native speaker's voice quality and a non-native accent. Finally, we show that this approach can be applied to non-parallel training data, achieving the same accent conversion performance.},

keywords = {Accent conversion, Deep learning, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{caio2019asl,

title = {Tradeoffs in the Efficient Detection of Sign Language Content in Video Sharing Sites},

author = {C. D. D. Monteiro and F. M. Shipman and III, S. Duggina and R. Gutierrez-Osuna},

url = {https://dl.acm.org/doi/10.1145/3325863

https://psi.engr.tamu.edu/wp-content/uploads/2020/04/caio2019asl.pdf},

year  = {2019},

date = {2019-06-01},

journal = {ACM Transactions on Accessible Computing},

volume = {12},

number = {2},

pages = {1-16},

keywords = {Computer vision, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{parnandi21017tac,

title = {Visual Biofeedback and Game Adaptation in Relaxation Skill Transfer},

author = {A Parnandi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2019/06/avinash-2019-tac.pdf},

year  = {2019},

date = {2019-05-17},

journal = {IEEE Transactions on Affective Computing},

volume = {10},

number = {2},

pages = {276 - 289},

keywords = {Electrodermal activity, Games, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{ahmed2018ijslp,

title = {Speech-driven mobile games for speech therapy: User experiences and feasibility},

author = {B Ahmed and P Monroe and A Hair and C-T Tan and R Gutierrez-Osuna and K J Ballard},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2019/06/ahmed-2018-ijslp.pdf

https://doi.org/10.1080/17549507.2018.1513562},

year  = {2018},

date = {2018-10-30},

journal = {International Journal of Speech-Language Pathology },

volume = {20},

number = {6},

pages = {644-658},

keywords = {Childhood apraxia of speech, Games, Health, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{mcKechnie2018review,

title = {Automated speech analysis tools for children’s speech production: A systematic literature review},

author = {J. McKechnie and B. Ahmed and R. Gutierrez-Osuna and P. Monroe and P. McCabe and K. J. Ballard},

url = {https://doi.org/10.1080/17549507.2018.1477991

https://psi.engr.tamu.edu/wp-content/uploads/2019/06/mckechnie-2018-ijslp.pdf},

year  = {2018},

date = {2018-09-02},

journal = {International Journal of Speech-Language Pathology},

volume = {20},

number = {6},

pages = {583–598},

keywords = {Childhood apraxia of speech, Health, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{jin2018-ac,

title = {Real-time Gas Mixture Analysis Using Mid-infrared Membrane Microcavities},

author = {Tiening Jin and Juntao Zhou and Zelun Wang and Ricardo Gutierrez-Osuna and Charles Ahn and Wonjun Hwang and Ken Park and Pao-Tai Lin },

url = {https://www.ncbi.nlm.nih.gov/pubmed/29509404},

year  = {2018},

date = {2018-03-08},

journal = {Analytical Chemistry},

volume = {90},

number = {7},

pages = {4348-4353},

keywords = {Chemical sensors, Infrared spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{wang2018jbhi,

title = {BioPad: Leveraging off-the-Shelf Video Games for Stress Self-Regulation},

author = {Z Wang and A Parnandi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/wang2018jbhi.pdf},

year  = {2018},

date = {2018-01-01},

journal = {IEEE Journal of Biomedical and Health Informatics, in press},

keywords = {Electrodermal activity, Games, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{christy2017emop,

title = {Mass Digitization of Early Modern Texts with Optical Character Recognition},

author = {M Christy and A Gupta and E Grumbach and L Mandel and R Furuta and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/christy2017jcch.pdf},

year  = {2017},

date = {2017-03-22},

journal = {ACM Journal on Computing and Cultural Heritage},

volume = {in press},

keywords = {OCR},

pubstate = {published},

tppubtype = {article}

}

@article{parnandi2017jbhi,

title = {Physiological modalities for relaxation skill transfer in biofeedback games},

author = {A Parnandi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avinash2017jbhi.pdf},

year  = {2017},

date = {2017-03-01},

journal = {Journal of Biomedical and Health Informatics},

volume = {in press},

keywords = {Electrodermal activity, Games, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{huang2016aca,

title = {Active wavelength selection for mixture identification with tunable mid-infrared detectors},

author = {J Huang and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2016aca.pdf},

year  = {2016},

date = {2016-08-08},

journal = {Analytica Chimica Acta},

volume = {in press},

keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{li2016sj,

title = {Using Field Asymmetric Ion Mobility Spectrometry for Odor Assessment of Automobile Interior Components},

author = {J Li and R Gutierrez-Osuna and R D Hodges and G Luckey and J Crowell and S S Schiffman and H T Nagle},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/li2016sj-1.pdf},

year  = {2016},

date = {2016-05-01},

journal = {IEEE Sensors Journal},

volume = {in press},

keywords = {Chemical sensors, Electronic nose, Machine olfaction},

pubstate = {published},

tppubtype = {article}

}

@article{aryal-2015-cls,

title = {Data driven articulatory synthesis with deep neural networks},

author = {S Aryal and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/aryal2016csl.pdf},

year  = {2016},

date = {2016-03-01},

urldate = {2016-03-01},

journal = {Computer Speech and Language},

volume = {36},

pages = {260-273},

keywords = {Accent conversion, Articulatory synthesis, Deep learning, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{parnandi2015taccess,

title = {Development of a remote therapy tool for childhood apraxia of speech},

author = {A Parnandi and V Karappa and T Lan and M Shahin and J McKechnie and K Ballard and B Ahmed and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/parnandi2015taccess.pdf},

year  = {2015},

date = {2015-11-01},

journal = {ACM Transactions on Accessible Computing},

volume = {7},

number = {3},

pages = {10:1-10:23},

keywords = {Childhood apraxia of speech, Games, Health, Mobile computing, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{ahmed2015taffc,

title = {ReBreathe: A calibration protocol that improves stress/relax classification by relabeling deep breathing relaxation exercises},

author = {B Ahmed and H Ali and J Choi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/ahmed2015taffc.pdf},

year  = {2015},

date = {2015-08-01},

journal = {IEEE Transactions on Affective Computing},

volume = {in press},

keywords = {Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{shahin2015specom,

title = {Tabby Talks: an automated tool for the assessment of childhood apraxia of speech},

author = {M Shahin and B Ahmed and A Parnandi and V Karappa and J McKechnie and K Ballard and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/shahin2015specom.pdf},

year  = {2015},

date = {2015-04-02},

urldate = {2015-04-02},

journal = {Speech Communication},

volume = {in press},

keywords = {Childhood apraxia of speech, Games, Health, Mobile computing, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{aryal2015jasa,

title = {Reduction of non-native accents through statistical parametric articulatory synthesis},

author = {S Aryal and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/aryal2015jasa.pdf},

year  = {2015},

date = {2015-01-23},

journal = {Journal of the Acoustical Society of America},

volume = {137},

number = {1},

pages = {433-446},

keywords = {Accent conversion, Articulatory synthesis, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{wang2015visual,

title = {Visual Tracking based on Online Sparse Feature Learning},

author = {Z Wang and J Wang and S Zhang and Y Gong},

editor = {Elsevier},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/wang2015visual.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Image and Vision Computing},

volume = {38},

number = {24--32},

abstract = {Various visual tracking approaches have been proposed for robust target tracking, among which using sparse representation of the tracking target yields promising performance. Some earlier works in this line used a fixed subset of features to compress the target's appearance, which has limited modeling capacity between the target and the background, and could not accommodate their appearance change over long period of time. In this paper, we propose a visual tracking method by modeling targets with online-learned sparse features. We first extract high dimensional Haar-like features as an over-completed basis set, and then solve the feature selection problem in an efficient L1-regularized sparse-coding process. The selected low-dimensional representation best discriminates the target from its neighboring background. Next we use a naive Bayesian classifier to select the most-likely target candidate by a binary classification process. The online feature selection process happens when there are significant appearance changes identified by a thresholding strategy. In this way, our proposed method could work for long tracking tasks. At the same time, our comprehensive experimental evaluation has shown that the proposed methods achieve excellent running speed and higher accuracy over many state-of-the-art approaches.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{huang2014sab,

title = {Active wavelength selection for mixture analysis with tunable infrared detectors},

author = {J Huang and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2014sab.pdf},

year  = {2015},

date = {2015-01-01},

journal = {Sensors and Actuators B: Chemical},

volume = {208},

pages = {245–257},

keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{avinash2014jmui,

title = {A Comparative Study of Game Mechanics and Control Laws for an Adaptive Physiological Game},

author = {A Parnandi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/avinash2014jmui.pdf},

year  = {2014},

date = {2014-04-29},

journal = {Journal on Multimodal User Interfaces},

keywords = {Electrodermal activity, Games, Health, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{alex2014epsr,

title = {Automatic Capacitor Bank Identification in Power Distribution Systems},

author = {A Perera-Lluna and K Manivannan and P Xu and R Gutierrez-Osuna and C Benner and B D Russell},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/alex2014epsr.pdf},

year  = {2014},

date = {2014-02-06},

journal = {Electric Power Systems Research},

volume = {in press},

keywords = {Other, Pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@article{yingwei2013prl,

title = {Context-sensitive Intra-class Clustering},

author = {Y Yu and Y Choe and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yingwei2013prl.pdf},

year  = {2014},

date = {2014-02-01},

journal = {Pattern Recognition Letters},

volume = {37},

pages = {85-93},

keywords = {Pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@article{rakesh2014cils,

title = {Active classification with arrays of tunable chemical sensors},

author = {R Gosangi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rakesh2014cils.pdf},

year  = {2014},

date = {2014-02-01},

journal = {Chemometrics and Intelligent Laboratory Systems},

volume = {132},

pages = {91-102},

keywords = {Active sensing, Metal-oxide sensors},

pubstate = {published},

tppubtype = {article}

}

@article{Shipman2013,

title = {Identifying Sign Language Videos in Video Sharing Sites},

author = {F Shipman and R Gutierrez-Osuna and C Monteiro},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/Shipman2013.pdf},

year  = {2013},

date = {2013-11-04},

journal = {ACM Transactions on Accessible Computing},

volume = {in press},

keywords = {Computer vision, Gestures},

pubstate = {published},

tppubtype = {article}

}

@article{rakeshmixturessab13,

title = {Active temperature modulation of metal-oxide sensors for quantitative analysis of gas mixtures},

author = {R Gosangi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rakeshmixturessab13.pdf},

year  = {2013},

date = {2013-04-15},

journal = {Sensors and Actuators B: Chemical},

volume = {185},

pages = {201-210},

keywords = {Active sensing, Chemical sensors, Metal-oxide sensors},

pubstate = {published},

tppubtype = {article}

}

@article{huang2012sj,

title = {Active Concentration-Independent Chemical Identification with a Tunable Infrared Sensor},

author = {J Huang and R Gosangi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/huang2012sj.pdf},

year  = {2012},

date = {2012-09-03},

journal = {Sensors Journal, IEEE},

abstract = {This paper presents an active sensing framework for concentration-independent identification of volatile chemicals using a tunable infrared interferometer. The framework operates in real time to generate a sequence of absorption lines that can best discriminate among a given set of chemicals. The active-sensing algorithm was previously developed to optimize temperature programs for metal-oxide chemosensors. Here, we adapt it to tune a non-dispersive infrared spectroscope based on a Fabry-Perot interferometer (FPI). We also extend this framework to allow the identification of chemical samples irrespective of their concentrations. Namely, we use non-negative matrix factorization (NMF) to create concentration-independent absorption profiles of different chemicals, and then employ linear least squares to fit sensor observations to the response profiles. We tested the framework on a simulated classification problem with 27 chemicals and compared against a passive sensing approach; active sensing consistently outperformed passive sensing in terms of classification performance for various sensing budgets and at various levels of sensor noise. We also validated the approach experimentally using a commercial FPI sensor and a database of eight household chemicals. Our results show the method is able to predict the sample identity irrespective of concentration.},

keywords = {Active sensing, Chemical sensors, Infrared spectroscopy},

pubstate = {published},

tppubtype = {article}

}

@article{choi2012titb,

title = {Development and Evaluation of an Ambulatory Stress Monitor Based on Wearable Sensors},

author = {J Choi and B Ahmed and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2012titb.pdf},

year  = {2012},

date = {2012-03-01},

journal = {IEEE Transactions on Information Technology in Biomedicine},

number = {99},

pages = {279	- 286},

publisher = {IEEE},

abstract = {Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects.},

keywords = {Electrodermal activity, Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{felps2012taslp,

title = {Foreign accent conversion through concatenative synthesis in the articulatory domain},

author = {D Felps and C Geng and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/felps2012taslp.pdf},

year  = {2012},

date = {2012-01-01},

journal = {IEEE Transactions on Audio, Speech and Language Processing},

abstract = {We propose a concatenative synthesis approach to the problem of foreign accent conversion. The approach consists of replacing the most accented portions of nonnative speech with alternative segments from a corpus of the speaker’s own speech based on their similarity to those from a reference native speaker. We propose and compare two approaches for selecting units, one based on acoustic similarity [e.g., mel frequency cepstral coeffi-cients (MFCCs)] and a second one based on articulatory similarity, as measured through electromagnetic articulography (EMA). Our hypothesis is that articulatory features provide a better metric for linguistic similarity across speakers than acoustic features. To test this hypothesis, we recorded an articulatory-acoustic corpus from a native and a nonnative speaker, and 

evaluated the two speech representations (acoustic versus articulatory) through a series of perceptual experiments. Formal listening tests indicate that the approach can achieve a 20% reduction in perceived accent, but also reveal a strong coupling between accent and speaker identity. To address this issue, we disguised original and resynthesized utterances by altering their average pitch and normalizing vocal tract length. An additional listening experiment supports the hypothesis that articulatory features are less speaker dependent than acoustic features.},

keywords = {Accent conversion, Speech},

pubstate = {published},

tppubtype = {article}

}

@article{choi2011removal,

title = {Removal of respiratory influences from heart rate variability in stress monitoring},

author = {J Choi and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/choi2011removal.pdf},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Sensors Journal},

volume = {11},

number = {11},

pages = {2649-2656},

publisher = {IEEE},

abstract = {This paper addresses a major weakness of traditional heart-rate-variability (HRV) analysis for the purpose of monitoring stress: sensitivity to respiratory influences. To address this issue, a linear system-identification model of the cardiorespiratory system using commercial heart rate monitors and respiratory sensors was constructed. Subtraction of respiratory driven fluctuations in heart rate leads to a residual signal where the effects of mental stress become more salient. We experimentally validated the effectiveness of this method on a binary discrimination problem with two conditions: mental stress of subjects performing cognitive tasks and a relaxation condition. In the process, we also propose a normalization method that can be used to compensate for ventilation differences between paced and spontaneous breathing. Our results suggest that, by separating respiration influences, the residual HRV has more discrimination power than traditional HRV analysis for the purpose of monitoring mental stress/load.},

keywords = {Health, Heart rate variability, Stress, Wearable sensors},

pubstate = {published},

tppubtype = {article}

}

@article{boulos2011web,

title = {Web GIS in practice X: a Microsoft Kinect natural user interface for Google Earth navigation},

author = {M N K Boulos and B J Blanchard and C Walker and J Montero and A Tripathy and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/boulos2011web.pdf},

year  = {2011},

date = {2011-01-01},

journal = {International Journal of Health Geographics},

volume = {10},

number = {1},

pages = {45},

publisher = {BioMed Central Ltd},

abstract = {This paper covers the use of depth sensors such as Microsoft Kinect and ASUS Xtion to provide a natural user interface (NUI) for controlling 3-D (three-dimensional) virtual globes such as Google Earth (including its Street View mode), Bing Maps 3D, and NASA World Wind. The paper introduces the Microsoft Kinect device, briefly describing how it works (the underlying technology by PrimeSense), as well as its market uptake and application potential beyond its original intended purpose as a home entertainment and video game controller. The different software drivers available for connecting the Kinect device to a PC (Personal Computer) are also covered, and their comparative pros and cons briefly discussed. We survey a number of approaches and application examples for controlling 3-D virtual globes using the Kinect sensor, then describe Kinoogle, a Kinect interface for natural interaction with Google Earth, developed by students at Texas A&M University. Readers interested in trying out the application on their own hardware can download a Zip archive (included with the manuscript as additional files 1, 2, & 3) that contains a 'Kinnogle installation package for Windows PCs'. Finally, we discuss some usability aspects of Kinoogle and similar NUIs for controlling 3-D virtual globes (including possible future improvements), and propose a number of unique, practical 'use scenarios' where such NUIs could prove useful in navigating a 3-D virtual globe, compared to conventional mouse/3-D mouse and keyboard-based interfaces.},

keywords = {Gestures},

pubstate = {published},

tppubtype = {article}

}

@article{rodriguez2011reverse,

title = {Reverse caricatures effects on three-dimensional facial reconstructions},

author = {J Rodriguez and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rodriguez2011reverse.pdf},

year  = {2011},

date = {2011-01-01},

journal = {Image and Vision Computing},

volume = {29},

number = {5},

pages = {329-334},

publisher = {Elsevier},

abstract = {Previous research has shown that familiarization with three-dimensional (3D) caricatures can help improve recognition of same-race and other-race faces, a result that may lead to new training tools in security applications. Since 3D facial scans are not generally available, here we sought to determine whether 3D reconstructions from 2D frontal images could be used for the same purpose. Our results suggest that, despite the high level of photographic realism achieved by current 3D facial reconstruction methods, additional research is needed in order to reduce reconstruction errors and capture the distinctive facial traits of an individual.},

keywords = {Face perception},

pubstate = {published},

tppubtype = {article}

}

@article{yu2010cogsci,

title = {Feature selection for inductive generalization},

author = {N Y Yu and T Yamauchi and H F Yang and Y L Chen and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yu2010cogsci.pdf},

year  = {2010},

date = {2010-01-01},

journal = {Cognitive science},

volume = {34},

number = {8},

pages = {1574--1593},

publisher = {Wiley Online Library},

abstract = {Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data.},

keywords = {Face perception},

pubstate = {published},

tppubtype = {article}

}

@article{gutierrez2010arac,

title = {Adaptive Microsensor Systems},

author = {R Gutierrez-Osuna and A Hierlemann},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez2010arac.pdf},

year  = {2010},

date = {2010-01-01},

journal = {Annual Review of Analytical Chemistry},

volume = {3},

pages = {255--276},

publisher = {Annual Reviews},

abstract = {We provide a broad review of approaches for developing chemosensor systems whose operating parameters can adapt in response to environmental changes or application needs. Adaptation may take place at the instrumentation level (e.g., tunable sensors) and at the data-analysis level (e.g., adaptive classifiers). We discuss several strategies that provide tunability at the device level: modulation of internal sensing parameters, such as frequencies and operation voltages; variation of external parameters, such as exposure times and catalysts; and development of compact microanalysis systems with multiple tuning options. At the data-analysis level, we consider adaptive filters for change, interference, and drift rejection; pattern classifiers that can adapt to changes in the statistical properties of training data; and active-sensing techniques that can tune sensing parameters in real time. We conclude with a discussion of future opportunities for adaptive sensing in wireless distributed sensor systems.},

keywords = {Active sensing, Chemical sensors},

pubstate = {published},

tppubtype = {article}

}

@article{felps2010talsp,

title = {Developing objective measures of foreign-accent conversion},

author = {D Felps and R Gutierrez-Osuna},

url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/felps2010talsp.pdf},

year  = {2010},

date = {2010-01-01},

journal = {Audio, Speech, and Language Processing, IEEE Transactions on},

volume = {18},

number = {5},

pages = {1030--1040},

publisher = {IEEE},

abstract = {Various methods have recently appeared to transform foreign-accented speech into its native-accented counterpart. Evaluation of these accent conversion methods requires extensive listening tests across a number of perceptual dimensions. This article presents three objective measures that may be used to assess the acoustic quality, degree of foreign accent, and speaker identity of accent-converted utterances. Accent conversion generates novel utterances: those of a foreign speaker with a native accent. Therefore, the acoustic quality in accent conversion cannot be evaluated with conventional measures of spectral distortion, which assume that a clean recording of the speech signal is available for comparison. Here we evaluate a single-ended measure of speech quality, lTV -T recommendation P.563 for narrow-band telephony. We also propose a measure of foreign accent that exploits a weakness of automatic speech recognizers: their sensitivity to foreign accents. Namely, we use phoneme-level match scores given by the HTK recognizer trained on a large number of English American speakers to obtain a measure of native accent. Finally, we propose a measure of speaker identity that projects acoustic vectors (e.g., Mel cepstral, F0) onto the linear discriminant that maximizes separability for a given pair of source and target speakers. The three measures are evaluated on a corpus of accent-converted utterances that had been previously rated through perceptual tests. Our results show that the three measures have a high degree of correlation with their corresponding subjective ratings, suggesting that they may be used to accelerate the development of foreign-accent conversion tools. Applications of these measures in the context of computer assisted pronunciation training and voice conversion are also discussed.},

keywords = {Accent conversion, Speech},

pubstate = {published},

tppubtype = {article}

}