State of the art in continuous speech recognition.

In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.

The Use of the Rey 15-Item Test and Recognition Trial to Evaluate Suboptimal Effort in Neuropsychological Assessment with a Pediatric Mild TBI Sample

The utilization of symptom validity tests (SVTs) in pediatric assessment is receiving increasing empirical support. The Rey 15-Item Test (FIT) is an SVT commonly used in adult assessment, with limited research in pediatric populations. Given that FIT classification statistics across studies to date have been quite variable, Boone, Salazar, Lu, Warner-Chacon, and Razani (2002) developed a recognition trial to use with the original measure to enhance accuracy. The current study aims to assess the utility of the FIT and recognition trial in a pediatric mild traumatic brain injury (TBI) sample (N = 112; M = 14.6 years), in which a suboptimal effort base rate of 17% has been previously established (Kirkwood & Kirk, 2010). All participants were administered the FIT as part of an abbreviated neuropsychological evaluation; failure on the Medical Symptom Validity Test (MSVT) was used as the criterion for suspect effort. The traditional adult cut-off score of(99%), but poor sensitivity (6%). When the recognition trial was also utilized, a combination score of(sensitivity = 64%, specificity = 93%). Results indicate that the FIT with recognition trial may be useful in the assessment of pediatric suboptimal effort, at least among relatively high functioning children following mild TBI.

eFisioTrack: a telerehabilitation environment based on motion recognition using accelerometry

The growing demand for physical rehabilitation processes can result in the rising of costs and waiting lists, becoming a threat to healthcare services’ sustainability. Telerehabilitation solutions can help in this issue by discharging patients from points of care while improving their adherence to treatment. Sensing devices are used to collect data so that the physiotherapists can monitor and evaluate the patients’ activity in the scheduled sessions. This paper presents a software platform that aims to meet the needs of the rehabilitation experts and the patients along a physical rehabilitation plan, allowing its use in outpatient scenarios. It is meant to be low-cost and easy-to-use, improving patients and experts experience. We show the satisfactory results already obtained from its use, in terms of the accuracy evaluating the exercises, and the degree of users’ acceptance. We conclude that this platform is suitable and technically feasible to carry out rehabilitation plans outside the point of care.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

Automaticity in L2 learning: Correlation between vocabulary proficiency and response time in word recognition

Automaticity (in this essay defined as short response time) and fluency in language use are closely connected to each other and some research has been conducted regarding some of the aspects involved. In fact, the notion of automaticity is still debated and many definitions and opinions on what automaticity is have been suggested (Andersson,1987, 1992, 1993, Logan, 1988, Segalowitz, 2010). One aspect that still needs more research is the correlation between vocabulary proficiency (a person’s knowledge about words and ability to use them correctly) and response time in word recognition. Therefore, the aim of this study has been to investigate this correlation using two different tests; one vocabulary size test (Paul Nation) and one lexical decision task (SuperLab) that measures both response time and accuracy. 23 Swedish students partaking in the English 7 course in upper secondary Swedish school were tested. The data were analyzed using a quantitative method where the average values and correlations from the test were used to compare the results. The correlations were calculated using Pearson’s Coefficient Correlations Calculator. The empirical study indicates that vocabulary proficiency is not strongly correlated with shorter response times in word recognition. Rather, the data indicate that L2 learners instead are sensitive to the frequency levels of the vocabulary. The accuracy (number of correct recognized words) and response times correlate with the frequency level of the tested words. This indicates that factors other than vocabulary proficiency are important for the ability to recognize words quickly.

Auditory stream segregation in cochlear implant listeners:measures based on temporal discrimination and interleaved melody recognition

The evidence that cochlear implant listeners routinely experience stream segregation is limited and equivocal. Streaming in these listeners was explored using tone sequences matched to the center frequencies of the implant’s 22 electrodes. Experiment 1 measured temporal discrimination for short (ABA triplet) and longer (12 AB cycles) sequences (tone/silence durations = 60/40 ms). Tone A stimulated electrode 11; tone B stimulated one of 14 electrodes. On each trial, one sequence remained isochronous, and tone B was delayed in the other; listeners had to identify the anisochronous interval. The delay was introduced in the second half of the longer sequences. Prior build-up of streaming should cause thresholds to rise more steeply with increasing electrode separation, but no interaction with sequence length was found. Experiment 2 required listeners to identify which of two target sequences was present when interleaved with distractors (tone/silence durations = 120/80 ms). Accuracy was high for isolated targets, but most listeners performed near chance when loudness-matched distractors were added, even when remote from the target. Only a substantial reduction in distractor level improved performance, and this effect did not interact with target-distractor separation. These results indicate that implantees often do not achieve stream segregation, even in relatively unchallenging tasks.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression

Objectives: Recently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data. Methods: We used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers. Results: The accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03). Conclusions: These results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Neural-Like Growing Networks in Intelligent System of Recognition of Images

The neural-like growing networks used in the intelligent system of recognition of images are under consideration in this paper. All operations made over the image on a pre-design stage and also classification and storage of the information about the images and their further identification are made extremely by mechanisms of neural-like networks without usage of complex algorithms requiring considerable volumes of calculus. At the conforming hardware support the neural network methods allow considerably to increase the effectiveness of the solution of the given class of problems, saving a high accuracy of result and high level of response, both in a mode of training, and in a mode of identification.

How different are the visual representations used for object recognition in middle childhood and adulthood?

Recent experimental studies have shown that development towards adult performance levels in configural processing in object recognition is delayed through middle childhood. Whilst partchanges to animal and artefact stimuli are processed with similar to adult levels of accuracy from 7 years of age, relative size changes to stimuli result in a significant decrease in relative performance for participants aged between 7 and 10. Two sets of computational experiments were run using the JIM3 artificial neural network with adult and 'immature' versions to simulate these results. One set progressively decreased the number of neurons involved in the representation of view-independent metric relations within multi-geon objects. A second set of computational experiments involved decreasing the number of neurons that represent view-dependent (nonrelational) object attributes in JIM3's Surface Map. The simulation results which show the best qualitative match to empirical data occurred when artificial neurons representing metric-precision relations were entirely eliminated. These results therefore provide further evidence for the late development of relational processing in object recognition and suggest that children in middle childhood may recognise objects without forming structural description representations.

Consistency and accuracy in memory: Effects of schema-typicality, retention interval and question-format

This study examined the effect of schemas on consistency and accuracy of memory across interviews, providing theoretical hypotheses explaining why inconsistencies may occur. The design manipulated schema-typicality of items (schema-typical and atypical), question format (free-recall, cued-recall and recognition) and retention interval (immediate/2 week and 2 week/4 week). Consistency, accuracy and experiential quality of memory were measured. ^ All independent variables affected accuracy and experiential quality of memory while question format was the only variable affecting consistency. These results challenge the commonly held notion in the legal arena that consistency is a proxy for accuracy. The study also demonstrates that other variables, such as item-typicality and retention interval have different effects on consistency and accuracy in memory. ^

A high efficient biometrics approach for unconstrained iris segmentation and recognition

This dissertation develops an innovative approach towards less-constrained iris biometrics. Two major contributions are made in this research endeavor: (1) Designed an award-winning segmentation algorithm in the less-constrained environment where image acquisition is made of subjects on the move and taken under visible lighting conditions, and (2) Developed a pioneering iris biometrics method coupling segmentation and recognition of the iris based on video of moving persons under different acquisitions scenarios. The first part of the dissertation introduces a robust and fast segmentation approach using still images contained in the UBIRIS (version 2) noisy iris database. The results show accuracy estimated at 98% when using 500 randomly selected images from the UBIRIS.v2 partial database, and estimated at 97% in a Noisy Iris Challenge Evaluation (NICE.I) in an international competition that involved 97 participants worldwide involving 35 countries, ranking this research group in sixth position. This accuracy is achieved with a processing speed nearing real time. The second part of this dissertation presents an innovative segmentation and recognition approach using video-based iris images. Following the segmentation stage which delineates the iris region through a novel segmentation strategy, some pioneering experiments on the recognition stage of the less-constrained video iris biometrics have been accomplished. In the video-based and less-constrained iris recognition, the test or subject iris videos/images and the enrolled iris images are acquired with different acquisition systems. In the matching step, the verification/identification result was accomplished by comparing the similarity distance of encoded signature from test images with each of the signature dataset from the enrolled iris images. With the improvements gained, the results proved to be highly accurate under the unconstrained environment which is more challenging. This has led to a false acceptance rate (FAR) of 0% and a false rejection rate (FRR) of 17.64% for 85 tested users with 305 test images from the video, which shows great promise and high practical implications for iris biometrics research and system design.