Two Case Examples of Reaching the Hard-to-Reach: Low Income Minority and LGBT Individuals

‘Hard-to-reach’ is a term primarily used by researchers to describe groups of people who have been historically difficult to find or contact. It is important for the public interest to include hard-to-reach groups in research because excluding certain sub-populations diminishes the ability to identify groups that potentially have the highest burden of illness and to develop an understanding of why group differences exist. Thus, the purposes of this paper are to: 1) describe the challenges in recruiting hard-to-reach population in two separate research studies; 2) discuss the strategies that were used to overcome those challenges; and 3) provide recommendations for researchers. This paper followed a case study research strategy, with the authors using two of their own research studies involving hard-to-reach populations as case studies. The research studies used in these case studies involved two different hard-to-reach groups—low-income ethnic minorities who were un- or under-insured and lesbian or bisexual women and transgender men. Two overarching themes were identified as barriers to reaching the population of interest: (1) gaining interest and (2) building trust. These themes add to the literature regarding the multi-prong approach that is needed to recruit members of hard-to-reach populations. Despite the authors having buy-in from stakeholders and a multi-prong recruiting approach, barriers to gaining the interest of potential participants included language in recruitment flyers, competing demands for time, and transportation to the data collection site. Building trust with interested study participants was also a large issue noted between both studies, especially concerning sensitive questions or cultural barriers regardless of the reliability and validity of the tools used in the study

Generalized Perceptual Linear Prediction (gPLP) Features for Animal Vocalization Analysis

A new feature extraction model, generalized perceptual linear prediction (gPLP), is developed to calculate a set of perceptually relevant features for digital signal analysis of animalvocalizations. The gPLP model is a generalized adaptation of the perceptual linear prediction model, popular in human speech processing, which incorporates perceptual information such as frequency warping and equal loudness normalization into the feature extraction process. Since such perceptual information is available for a number of animal species, this new approach integrates that information into a generalized model to extract perceptually relevant features for a particular species. To illustrate, qualitative and quantitative comparisons are made between the species-specific model, generalized perceptual linear prediction (gPLP), and the original PLP model using a set of vocalizations collected from captive African elephants (Loxodonta africana) and wild beluga whales (Delphinapterus leucas). The models that incorporate perceptional information outperform the original human-based models in both visualization and classification tasks

Automatic Classification of African Elephant (\u3cem\u3eLoxodonta africana\u3c/em\u3e) Follicular and Luteal Rumbles

Recent research in African elephant vocalizations has shown that there is evidence for acoustic differences in the rumbles of females based on the phase of their estrous cycle (1). One reason for these differences might be to attract a male for reproductive purposes. Since rumbles have a fundamental frequency near 10Hz, they attenuate slowly and can be heard over a distance of several kilometers. This research exploits differences in the rumbles to create an automatic classification system that can determine whether a female rumble was made during the luteal or follicular phase of the ovulatory cycle. This system could be used as the basis for a non-invasive technique to determine the reproductive status of a female African elephant. The classification system is based on current state-of-the-art human speech processing systems. Standard features and models are applied with the necessary modifications to account for the physiological, anatomical and language differences between humans and African elephants. The long-term goal of this research is to develop a universal analysis framework and robust feature set for animal vocalizations that can be applied to many species. This research represents an application of this framework. The vocalizations used for this study were collected from a group of three female captive elephants. The elephants are fitted with radio-transmitting microphone collars and released into one of three naturalistic yards on a daily basis. Although this data collection setup is good for determining the speaker of each vocalization, it suffers from many potential noise sources such as RF interference, passing vehicles, and the flapping of the elephant’s ears against the collar

Limiting operations for sequences of quantum random variables and a convergence theorem for quantum martingales

We study quantum random variables and generalize several classical limit results to the quantum setting. We prove a quantum analogue of Lebesgue's dominated convergence theorem and use it to prove a quantum martingale convergence theorem. This quantum martingale convergence theorem is of particular interest since it exhibits non-classical behaviour; even though the limit of the martingale exists and is unique, it is not explicitly identifiable. However, we provide a partial classification of the limit through a study of the space of all quantum random variables having quantum expectation zero.Comment: 11 pages, 0 figure

On the Conjectures Regarding the 4-Point Atiyah Determinant

For the case of 4 points in Euclidean space, we present a computer aided proof of Conjectures II and III made by Atiyah and Sutcliffe regarding Atiyah's determinant along with an elegant factorization of the square of the imaginary part of Atiyah's determinant

The influence of microstructure on the tensile behavior of an aluminum metal matrix composite

The relationship between tensile properties and microstructure of a powder metallurgy aluminum alloy, 2009 was examined. The alloy was investigated both unreinforced and reinforced with 15 v/o SiC whiskers or 15 v/o SiC particulate to form a discontinuous metal matrix composite (MMC). The materials were investigated in the as-fabricated condition and in three different hot-rolled sheet thicknesses of 6.35, 3.18, and 1.8 mm. Image analysis was used to characterize the morphology of the reinforcements and their distributions within the matrix alloy. Fractographic examinations revealed that failure was associated with the presence of microstructural inhomogeneities which were related to both the matrix alloy and to the reinforcement. The results from these observations together with the matrix tensile data were used to predict the strengths and moduli of the MMC's using relatively simple models. The whisker MMC could be modeled as a short fiber composite and an attempt was made to model the particulate MMC as a dispersion/dislocation hardened alloy

Tracking Articulator Movements Using Orientation Measurements

This paper introduces a new method to track articulator movements, specifically jaw position and angle, using 5 degree of freedom (5 DOF) orientation data. The approach uses a quaternion rotation method to accomplish this jaw tracking during speech using a single senor on the mandibular incisor. Data were collected using the NDI Wave Speech Research System for one pilot subject with various speech tasks. The degree of jaw rotation from the proposed approach is compared with traditional geometric calculation. Results show that the quaternion based method is able to describe jaw angle trajectory and gives more accurate and smooth estimation of jaw kinematics

Sensorimotor Adaptation of Speech Using Real-time Articulatory Resynthesis

Sensorimotor adaptation is an important focus in the study of motor learning for non-disordered speech, but has yet to be studied substantially for speech rehabilitation. Speech adaptation is typically elicited experimentally using LPC resynthesis to modify the sounds that a speaker hears himself producing. This method requires that the participant be able to produce a robust speech-acoustic signal and is therefore not well-suited for talkers with dysarthria. We have developed a novel technique using electromagnetic articulography (EMA) to drive an articulatory synthesizer. The acoustic output of the articulatory synthesizer can be perturbed experimentally to study auditory feedback effects on sensorimotor learning. This work aims to compare sensorimotor adaptation effects using our articulatory resynthesis method with effects from an established, acoustic-only method. Results suggest that the articulatory resynthesis method can elicit speech adaptation, but that the articulatory effects of the two methods differ

Parallel Reference Speaker Weighting for Kinematic-Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and open problem. This paper proposes a novel approach to acoustic-to-articulatory inversion, Parallel Reference Speaker Weighting (PRSW), which requires no kinematic data for the target speaker and a small amount of acoustic adaptation data. PRSW hypothesizes that acoustic and kinematic similarities are correlated and uses speaker-adapted articulatory models derived from acoustically derived weights. The system was assessed using a 20-speaker data set of synchronous acoustic and Electromagnetic Articulography (EMA) kinematic data. Results demonstrate that by restricting the reference group to a subset consisting of speakers with strong individual speaker-dependent inversion performance, the PRSW method is able to attain kinematic-independent acoustic-to-articulatory inversion performance nearly matching that of the speaker-dependent model, with an average correlation of 0.62 versus 0.63. This indicates that given a sufficiently complete and appropriately selected reference speaker set for adaptation, it is possible to create effective articulatory models without kinematic training data