Phone Dialing Research Articles

Construct and Evaluate a Phone Dialing System Leveraging SSVEP Brain-Computer Interface

Construct and Evaluate a Phone Dialing System Leveraging SSVEP Brain-Computer Interface

Location Transparency Call (LTC) System: An Intelligent Phone Dialing System Based on the Phone of Things (PoT) Architecture

Phone of Things (PoT) extends the connectivity options for IoT systems by leveraging the ubiquitous phone network infrastructure, making it part of the IoT architecture. PoT enriches the connectivity options of IoT while promoting its affordability, accessibility, security, and scalability. PoT enables incentive IoT applications that can result in more innovative homes, office environments, and telephony solutions. This paper presents the Location Transparency Call (LTC) system, an intelligent phone dialing system for businesses based on the PoT architecture. The LTC system intelligently mitigates the impact of missed calls on companies and provides high availability and dynamic reachability to employees within the premises. LTC automatically forwards calls to the intended employees to the closest phone extensions at their current locations. Location transparency is achieved by actively maintaining and dynamically updating a real-time database that maps the persons’ locations using the RFID tags they carry. We demonstrate the system’s feasibility and usability and evaluate its performance through a fully-fledged prototype representing its hardware and software components that can be applied in real situations at large scale.

A comparative case study of neural network training by using frame-level cost functions for automatic speech recognition purposes in Spanish

Training procedures of a deep neural network are still an area with ample research possibilities and constant improvement either to increase its efficiency or its time performance. One of the lesser-addressed components is its objective function, which is an underlying aspect to consider when there is the necessity to achieve better error rates in the area of automatic speech recognition. The aim of this paper is to present two new variations of the frame-level cost function for training a deep neural network with the purpose of obtaining superior word error rates in speech recognition applied to a case study in Spanish. The first proposed function is a fusion between the boosted cross-entropy and the so called cross-entropy/log-posterior-ratio. The main idea is to jointly emphasize the prediction of difficult/crucial frames provided by a boosting factor and at the same time enlarge the distance between the target senone and its closest competitor. The second proposal is a fusion between the non-uniform mapped cross-entropy and the cross-entropy/log-posterior-ratio. This function utilizes both the mapped function to enhance the frames that have ambiguity in their belonging to specific senones and the log-posterior-ratio with the purpose of separating the target senone against the most competing tied tri-phone state. The proposed approaches are compared against those frame-level cost functions discussed in the state of the art. This comparative has been made by using a personalized mid-vocabulary speaker-independent voice corpus. This dataset is employed for the recognition of digit strings and personal name lists in Spanish from the northern central part of Mexico on a connected-words phone dialing task. A relative word error rate improvement of 15.14% and 12.30% is obtained with the two proposed approaches, respectively, against the plain well-established cross-entropy loss function.

Training deep neural networks with non-uniform frame-level cost function for automatic speech recognition

The aim of this paper is to exhibit two new variations of the frame-level cost function for training a deep neural network in order to achieve better word error rates in speech recognition. Optimization methods and their minimization functions are underlying aspects to consider when someone is working on neural nets, and hence their improvement is one of the salient objectives of researchers, and this paper deals in part with such a situation. The first proposed framework is based on the concept of extropy, the complementary dual function of an uncertainty measure. The conventional cross-entropy function can be mapped to a non-uniform loss function based on its corresponding extropy, enhancing the frames that have ambiguity in their belonging to specific senones. The second proposal makes a fusion of the presented mapped cross-entropy function and the idea of boosted cross-entropy, which emphasizes those frames with low target posterior probability. The proposed approaches have been performed by using a personalized mid-vocabulary speaker-independent voice corpus. This dataset is employed for recognition of digit strings and personal name lists in Spanish from the northern central part of Mexico on a connected-words phone dialing task. A relative word error rate improvement of $12.3\%$ and $10.7\%$ is obtained with the two proposed approaches, respectively, with regard to the conventional well-established cross-entropy objective function.

Semiparametric Bayesian models for evaluating time-variant driving risk factors using naturalistic driving data and case-crossover approach.

Driver behavior is a major contributing factor for traffic crashes, a leading cause of death and injury in the United States. The naturalistic driving study (NDS) revolutionizes driver behavior research by using sophisticated nonintrusive in-vehicle instrumentation to continuously record driving data. This paper uses a case-crossover approach to evaluate driver-behavior risk. To properly model the unbalanced and clustered binary outcomes, we propose a semiparametric hierarchical mixed-effect model to accommodate both among-strata and within-stratum variations. This approach overcomes several major limitations of the standard models, eg, constant stratum effect assumption for conditional logistic model. We develop 2 methods to calculate the marginal conditional probability. We show the consistency of parameter estimation and asymptotic equivalence of alternative estimation methods. A simulation study indicates that the proposed model is more efficient and robust than alternatives. We applied the model to the 100-Car NDS data, a large-scale NDS with 102 participants and 12-month data collection. The results indicate that cell phone dialing increased the crash/near-crash risk by 2.37 times (odds ratio: 2.37, 95% CI, 1.30-4.30) and drowsiness increased the risk 33.56 times (odds ratio: 33.56, 95% CI, 21.82-52.19). This paper provides new insight into driver behavior risk and novel analysis strategies for NDS studies.

Walking stability during cell phone use in healthy adults

The number of falls and/or accidental injuries associated with cellular phone use during walking is growing rapidly. Understanding the effects of concurrent cell phone use on human gait may help develop safety guidelines for pedestrians. It was shown previously that older adults had more pronounced dual-task interferences than younger adults when concurrent cognitive task required visual information processing. Thus, cell phone use might have greater impact on walking stability in older than in younger adults. This study examined gait stability and variability during a cell phone dialing task (phone) and two classic cognitive tasks, the Paced Auditory Serial Addition Test (PASAT) and Symbol Digit Modalities Test (SDMT). Nine older and seven younger healthy adults walked on a treadmill at four different conditions: walking only, PASAT, phone, and SDMT. We computed short-term local divergence exponent (LDE) of the trunk motion (local stability), dynamic margins of stability (MOS), step spatiotemporal measures, and kinematic variability. Older and younger adults had similar values of short-term LDE during all conditions, indicating that local stability was not affected by the dual-task. Compared to walking only, older and younger adults walked with significantly greater average mediolateral MOS during phone and SDMT conditions but significantly less ankle angle variability during all dual-tasks and less knee angle variability during PASAT. The current findings demonstrate that healthy adults may try to control foot placement and joint kinematics during cell phone use or another cognitive task with a visual component to ensure sufficient dynamic margins of stability and maintain local stability.

A study of young adults examining phone dialing while driving using a touchscreen vs. a button style flip-phone

A simulation study compared 36 young adult drivers’ eye movements, driving behavior, and task completion time while dialing a flip-phone with tactile pushbuttons and an iPhone which provides a touchscreen interface. Once recruited, information on experience with different phone types was collected from each participant, which was then used as a covariate in statistical analysis. Participants who often use a traditional manual button phone completed the dialing task faster when using the flip-phone compared to touchscreen users using the iPhone. The flip phone, in general, resulted in fewer glances to the device than the iPhone. The mean number of glances greater than 1.6s with the iPhone was 2.1 times the mean number with the flip phone. Further, females using the flip phone had the highest percentage of time spent with eyes on the road and the lowest likelihood of exhibiting long duration off-road glances (i.e., greater than 1.6s and greater than 2s). In terms of driving behavior, non-touchscreen users were found to slow down both when they were dialing on the flip phone and the iPhone, whereas touchscreen users slowed down only when they were dialing on the flip phone. Standard deviation of lane position was the highest when not dialing a phone, followed by when dialing the flip phone, and was the lowest when dialing the iPhone. Advantages appear to exist in a traditional tactile manual interface in terms of allocation of visual attention and possibly in compensatory behavior.

The development of EEG-based brain computer interfaces: potential and challenges

Brain computer interface (BCI) technology potentially opens the door to allowing our brain to influence the outside world without the use of muscles. This research concentrates on developing new augmentative communication and control technologies for those with severe neuromuscular disorders, such as stroke and spinal cord injury. Most BCIs decode the intent of the user from the brain's electrical activity as recorded from the scalp (electroencephalography, EEG). The EEG signal is very difficult to process, but many advances have been made to translate the signal into commands. At The University of Auckland, EEG-based BCIs have been under development for the past few years. Our laboratory has produced excellent results including an EEG controlled video game, an EEG phone dialing system, and an EEG active rehabilitation exercise. Research is ongoing in this area, with new projects starting for control of prostheses or rehabilitation robots in clinical trials.

Picture browsing non-touch interaction methods for smartphones using an accelerometer and camera with a focus on phone dialing

This paper proposes various interaction methods for smartphones that allow users to call an intended contact without touching the smartphone screen. Existing applications allow users to answer phone calls without touching the screen--by shaking the phone, for example--but do not allow users to make phone calls. The proposed interaction allows users to select and call an intended contact by utilizing the iPhone's accelerometer. The interaction also involves video camera scanning for commands to switch between group-selection and individual-selection modes to facilitate the selection of the call candidate. Furthermore, the proposed interaction secures transparency by displaying the camera's video stream on the smartphone screen. In order to evaluate the efficacy of the interaction, an application using the interaction was developed, and two simple experiments were conducted, in which participants were asked to make phone calls using the application. The success rate was 98 %, and user satisfaction with the proposed interaction was approximately 90 %. Therefore, the results showed that the proposed interaction could be an effective solution to allow users to make phone calls in situations where they cannot physically touch the iPhone screen. Furthermore, this solution could be used in many fields that need interactions with users in mobile applications.

Implementation of an Intelligent Telephone Dialing System Based on the Control of Steady-State Visual Evoked Potentials

With the development of brain-computer interface (BCI) technology, researchers are now attempting to put current BCI techniques into practical applications. This paper proposes an intelligent telephone dialing system controlled by a steady-state visual evoked potential (SSVEP) based BCI. This dialing system consists of four parts: a visual stimulator, EEG signal acquisition and amplification, PC real-time processing system and an auto telephone dialing module. Seven volunteers participated in an experiment designed to test the performance of the dialing system. The experimental results suggested that all subjects succeeded in the phone dialing with mean accuracy rate of 94.5% and mean bit rate of 33.31 bit/min.

O-009 Younger age of menopause in women with cerebral aneurysms

BackgroundThe incidence of subarachnoid hemorrhage in women surges following menopause. Estrogen fluctuations have been implicated in cerebral aneurysm formation, growth and rupture and is thought to explain the well known...

Younger age of menopause in women with cerebral aneurysms

BackgroundThe incidence of subarachnoid hemorrhage in women surges following menopause. Estrogen fluctuations have been implicated in cerebral aneurysm formation, growth and rupture and are thought to explain the well-known gender...

Oral contraceptive and hormone replacement therapy in women with cerebral aneurysms

BackgroundIt is well known that cerebral aneurysms occur more frequently in women, with numerous studies suggesting a role for hormones in aneurysm pathogenesis. Estrogen promotes normal physiologic vascular endothelial function...

Effects of Voice Technology on Test Track Driving Performance: Implications for Driver Distraction

This work compares the degradation in driving performance associated with secondary tasks performed with voice-based and visual/manual interfaces, including radio tuning, phone dialing, and more complex tasks involving a sequence of interactions with an in-vehicle computer system. Twenty-one participants drove an instrumented vehicle while performing a combination of car-following, peripheral target detection, and secondary tasks on a closed test track. Drivers compensated for increased task demands associated with secondary tasks by increasing their following distance. Performing secondary tasks also resulted in significant decrements to vehicle control, target detection, and car-following performance. The voice-based interface helped reduce the distracting effects of secondary task performance. Modest improvements were observed for measures of vehicle control and target detection but not for car following. The results indicated that performing in-vehicle tasks required diversion of both peripheral (visual and manual) and attentional (cognitive) resources from driving. The voice-based interface reduced the peripheral impairment but did not appreciably reduce the attentional impairment. Actual or potential applications of this research include improvements to the design of invehicle information systems and the development of evaluation protocols to assess their distraction potential.

A Multitasking General Executive for Compound Continuous Tasks

As cognitive architectures move to account for increasingly complex real-world tasks, one of the most pressing challenges involves understanding and modeling human multitasking. Although a number of existing models now perform multitasking in real-world scenarios, these models typically employ customized executives that schedule tasks for the particular domain but do not generalize easily to other domains. This article outlines a general executive for the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture that, given independent models of individual tasks, schedules and interleaves the models' behavior into integrated multitasking behavior. To demonstrate the power of the proposed approach, the article describes an application to the domain of driving, showing how the general executive can interleave component subtasks of the driving task (namely, control and monitoring) and interleave driving with in-vehicle secondary tasks (radio tuning and phone dialing).

Automatic speech recognition

Great strides have been made in the development of automatic speech recognition (ASR) technology over the past thirty years. Most of this effort has been centered around the extension and improvement of Hidden Markov Model (HMM) approaches to ASR. Current commercially-available and industry systems based on HMMs can perform well for certain situational tasks that restrict variability such as phone dialing or limited voice commands. However, the holy grail of ASR systems is performance comparable to humans—in other words, the ability to automatically transcribe unrestricted conversational speech spoken by an infinite number of speakers under varying acoustic environments. This goal is far from being reached. Key to the success of ASR is effective modeling of variability in the speech signal. This tutorial will review the basics of ASR and the various ways in which our current knowledge of speech production, speech perception and prosody can be exploited to improve robustness at every level of the system.

Investigating the Effect of Performing Voice Recognition Tasks on the Detection of Forward and Peripheral Events

This research investigates the ability of a driver to detect forward and peripheral events while driving and interacting with a voice recognition system. Thirty-seven participants took part in a driving experiment on a closed highway. Participants performed phone dialing tasks using either a voice recognition system simulation or a manual cell phone while driving. Participants also monitored two lights: one mounted on the hood of the vehicle and one on the left side mirror. Participants were instructed to press a foot pedal when a light appeared (i.e., event). Data were collected on drivers' glance locations as well as their responses to events. Results suggested that voice recognition dialing generally resulted in better visual scanning of the environment and response time to events than manual dialing. In addition, for younger participants, performance using the voice recognition system was similar to baseline driving, while older participants showed performance decrements for all tasks relative to baseline driving.

Driver Distraction: Evaluation with Event Detection Paradigm

The effects of eight in-vehicle tasks on driver distraction were measured in a large, moving-base driving simulator. Forty-eight adults, ranging in age from 35 to 66, and 15 teenagers participated in the simulated drive. Hand-held and hands-free versions of phone dialing, voicemail retrieval, and incoming calls represented six of the eight tasks. Manual radio tuning and climate control adjustment were also included to allow comparison with tasks that have traditionally been present in vehicles. During the drive the participants were asked to respond to sudden movements in surrounding traffic. The driver’s ability to detect these sudden movements or events changed with the nature of the in-vehicle tasks that were being performed. Driving performance measures such as lane violations and heading error were also computed. The performance of the adult group was compared with the performance of the teenage drivers. Compared with the adults, the teens were found to choose unsafe following distances, have poor vehicle control skills, and be more prone to distraction from hand-held phone tasks.

The Effects of Voice Technology on Test Track Driving Performance: Implications for Driver Distraction

Twenty-one subjects completed two sets of (8) laps around a 7.5-mile test track during two 4-hour sessions. They drove an instrumented vehicle while performing a combination of car following, peripheral target detection, and secondary (in-vehicle) tasks of varying complexity. Subjects performed one set of laps with each of two interfaces, voice-based and visual/manual. Secondary tasks comprised three categories including baseline tasks (radio tuning, phone dialing), simple tasks (message retrieval plus voice memo creation), and complex tasks (simple task components plus phone dialing and information retrieval from automated phone systems). Measures of driving performance, target-detection, secondary task performance and eye movements were recorded. Analyses were conducted to determine whether the voice-based interface reduced the relative distraction potential for secondary tasks of varying complexity. Generally, differences between tasks were stronger than differences between interface conditions. Measures of car-following performance, target detection, and secondary task performance revealed differences attributable to task complexity. Differences between the two interfaces were observed on peripheral target detection measures and on several driving performance measures. Overall, the benefits of using the voice-based interface were not large enough to appreciably reduce the distraction potential associated with performing the secondary tasks in the car-following scenario.

Development of a Test Protocol to Demonstrate the Effects of Secondary Tasks on Closed-Course Driving Performance

To assess the relative distraction potential associated with a variety of in-vehicle tasks, we developed a test protocol that provides an objective basis for demonstrating the tradeoffs between primary and secondary task performance in driving. The protocol includes two summary scores, one representing driving performance and one representing secondary-task performance. Driving performance was evaluated on a 1.3-mile test course, which included straight and curved road segments, a signalized intersection, a simulated work zone, two changeable-message signs, and six unexpected events. Secondary tasks included mental arithmetic, reading, writing, phone dialing, CD changing, route-guidance destination entry, eating, drinking and grooming. Twelve subjects drove an instrumented vehicle for 9 or 10 timed laps of the test course, each while performing a different secondary task or none (baseline). On average, we found a 15% decrement in driving performance scores on laps in which subjects engaged in a secondary task. Driving performance decrements were observed on 88% of these trials. Differences were observed between different secondary tasks. The pilot test results demonstrate the potential usefulness of the test protocol for assessing tradeoffs between primary and secondary tasks in driving.