Sermons, discourses, and epistolary opinions on religious matters by Gershom Seixas

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Boys and girls in religious school, handwriting of Reverend Gershom Seixas

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Subscription to K. K. S. I. religious school

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Report of Gershom Seixas regarding religious school

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Variation in the Understanding of Lutheranism and Its Implications for Religious Education : Meaning Discernment of Students and Teachers in Finnish Upper Secondary Schools

The study investigated variation in the ways in which a group of students and teachers of Evangelical Lutheran religious education in Finnish upper secondary schools understand Lutheranism and searched for educational implications for learning in religious education. The aim of understanding the qualitative variation in understanding Lutheranism was explored through the relationship between the following questions, which correspond to the results reported in the following original refereed publications: 1) How do Finnish students understand Lutheranism? 2) How do Finnish teachers of religious education constitute the meaning of Lutheranism? 3) How could phenomenography and the Variation Theory of Learning contribute to learning about and from religion in the context of Finnish Lutheran Religious Education as compared to religious education in the UK? Two empirical studies (Hella, 2007; Hella, 2008) were undertaken from a phenomenographic research perspective (e.g., Marton, 1981) and the Variation Theory of Learning (e.g., Marton & Tsui et al. 2004) that developed from it. Data was collected from 63 upper secondary students and 40 teachers of religious education through written tasks with open questions and complementary interviews with 11 students and 20 teachers for clarification of meanings. The two studies focused on the content and structure of meaning discernment in students and teachers expressed understandings of Lutheranism. Differences in understandings are due to differences in the meanings that are discerned and focused on. The key differences between the ways students understand varied from understanding Lutheranism as a religion to personal faith with its core in mercy. The logical relationships between the categories that describe variation in understanding express a hierarchy of ascending complexity, according to which more developed understandings are inclusive of less developed ones. The ways the teachers understand relate to student s understandings in a sequential manner. Phenomenography and Variation Theory were discussed in the context of religious education in Finland and the UK in relation to the theoretical notion of learning about and from religion (Hella & Wright, 2008). The thesis suggests that variation theory enables religious educators to recognise the unity of learning about and from religion, as learning is always learning about something and involves simultaneous engagement with the object of learning and development as a person. The study also suggests that phenomenography and variation theory offer a means by which it is possible for academics, policy makers, curriculum designers, teachers and students to learn to discern different ways of understanding the contested nature of religions. Keywords: Lutheranism, understanding, variation, teaching, learning, phenomenography, religious education

Lecture by Jacques Judah Lyons on the history of the Jews, given before the Hebrew Religious and Literary Library Association

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Spirituality in Indian University students and its associations with socioeconomic status, religious background, social support, and mental health

The present study aimed to understand spirituality and its relationships with socioeconomic status (SES), religious background, social support, and mental health among Indian university students. It was hypothesized that: - (1) female university students will be more spiritual than male university students, - (2) four domains of spirituality will differ significantly across socioeconomic and religious background of the university students in addition to social support, and; - (3) there will be a positive relationship between spirituality and mental health of university students, irrespective of gender. A group of 475 postgraduate students aged 20–27 years, 241 males and 234 females, from various disciplines of Pondicherry University, India, participated in the study. Students’ background was collected using a structured questionnaire. Overall spirituality and its four dimensions were measured using the Spirituality Attitude Inventory, while mental health status was estimated based on scores of the psychological subscale of the WHO Quality of Life Questionnaire. Female students were significantly more spiritual than male students, particularly in spiritual practice and sense of purpose/connection. Hindu religion and lower family income were associated with lower spirituality. Higher spirituality was associated with congenial family environment and more support from teachers and classmates. There was a strong association between overall spirituality and two spirituality domains (spiritual belief and sense of purpose/connection) with better mental health. Findings suggest an opportunity for open dialogue on spirituality for university students as part of their mental health and support services that fosters a positive mind set and enhancement of resilience.

Robust and High-resolution Voiced/Unvoiced Classification in Noisy Speech Using A Signal Smoothness Criterion

We propose a novel technique for robust voiced/unvoiced segment detection in noisy speech, based on local polynomial regression. The local polynomial model is well-suited for voiced segments in speech. The unvoiced segments are noise-like and do not exhibit any smooth structure. This property of smoothness is used for devising a new metric called the variance ratio metric, which, after thresholding, indicates the voiced/unvoiced boundaries with 75% accuracy for 0dB global signal-to-noise ratio (SNR). A novelty of our algorithm is that it processes the signal continuously, sample-by-sample rather than frame-by-frame. Simulation results on TIMIT speech database (downsampled to 8kHz) for various SNRs are presented to illustrate the performance of the new algorithm. Results indicate that the algorithm is robust even in high noise levels.

Two Stage Transform Vector Quantization of LSFs for Wideband Speech Coding

We investigate the use of a two stage transform vector quantizer (TSTVQ) for coding of line spectral frequency (LSF) parameters in wideband speech coding. The first stage quantizer of TSTVQ, provides better matching of source distribution and the second stage quantizer provides additional coding gain through using an individual cluster specific decorrelating transform and variance normalization. Further coding gain is shown to be achieved by exploiting the slow time-varying nature of speech spectra and thus using inter-frame cluster continuity (ICC) property in the first stage of TSTVQ method. The proposed method saves 3-4 bits and reduces the computational complexity by 58-66%, compared to the traditional split vector quantizer (SVQ), but at the expense of 1.5-2.5 times of memory.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

Seeing and hearing speech, sounds, and signs: functional magnetic resonance studies on fluent and dyslexic readers

Speech has both auditory and visual components (heard speech sounds and seen articulatory gestures). During all perception, selective attention facilitates efficient information processing and enables concentration on high-priority stimuli. Auditory and visual sensory systems interact at multiple processing levels during speech perception and, further, the classical motor speech regions seem also to participate in speech perception. Auditory, visual, and motor-articulatory processes may thus work in parallel during speech perception, their use possibly depending on the information available and the individual characteristics of the observer. Because of their subtle speech perception difficulties possibly stemming from disturbances at elemental levels of sensory processing, dyslexic readers may rely more on motor-articulatory speech perception strategies than do fluent readers. This thesis aimed to investigate the neural mechanisms of speech perception and selective attention in fluent and dyslexic readers. We conducted four functional magnetic resonance imaging experiments, during which subjects perceived articulatory gestures, speech sounds, and other auditory and visual stimuli. Gradient echo-planar images depicting blood oxygenation level-dependent contrast were acquired during stimulus presentation to indirectly measure brain hemodynamic activation. Lip-reading activated the primary auditory cortex, and selective attention to visual speech gestures enhanced activity within the left secondary auditory cortex. Attention to non-speech sounds enhanced auditory cortex activity bilaterally; this effect showed modulation by sound presentation rate. A comparison between fluent and dyslexic readers' brain hemodynamic activity during audiovisual speech perception revealed stronger activation of predominantly motor speech areas in dyslexic readers during a contrast test that allowed exploration of the processing of phonetic features extracted from auditory and visual speech. The results show that visual speech perception modulates hemodynamic activity within auditory cortex areas once considered unimodal, and suggest that the left secondary auditory cortex specifically participates in extracting the linguistic content of seen articulatory gestures. They are strong evidence for the importance of attention as a modulator of auditory cortex function during both sound processing and visual speech perception, and point out the nature of attention as an interactive process (influenced by stimulus-driven effects). Further, they suggest heightened reliance on motor-articulatory and visual speech perception strategies among dyslexic readers, possibly compensating for their auditory speech perception difficulties.

Joint decoding of multiple speech patterns for robust speech recognition

We are addressing a new problem of improving automatic speech recognition performance, given multiple utterances of patterns from the same class. We have formulated the problem of jointly decoding K multiple patterns given a single Hidden Markov Model. It is shown that such a solution is possible by aligning the K patterns using the proposed Multi Pattern Dynamic Time Warping algorithm followed by the Constrained Multi Pattern Viterbi Algorithm The new formulation is tested in the context of speaker independent isolated word recognition for both clean and noisy patterns. When 10 percent of speech is affected by a burst noise at -5 dB Signal to Noise Ratio (local), it is shown that joint decoding using only two noisy patterns reduces the noisy speech recognition error rate to about 51 percent, when compared to the single pattern decoding using the Viterbi Algorithm. In contrast a simple maximization of individual pattern likelihoods, provides only about 7 percent reduction in error rate.

GMM based Bayesian approach to speech enhancement in signal transform domain

Considering a general linear model of signal degradation, by modeling the probability density function (PDF) of the clean signal using a Gaussian mixture model (GMM) and additive noise by a Gaussian PDF, we derive the minimum mean square error (MMSE) estimator. The derived MMSE estimator is non-linear and the linear MMSE estimator is shown to be a special case. For speech signal corrupted by independent additive noise, by modeling the joint PDF of time-domain speech samples of a speech frame using a GMM, we propose a speech enhancement method based on the derived MMSE estimator. We also show that the same estimator can be used for transform-domain speech enhancement.

New Method for Delexicalization and its Application to Prosodic Tagging for Text-to-Speech Synthesis

This paper describes a new flexible delexicalization method based on glottal excited parametric speech synthesis scheme. The system utilizes inverse filtered glottal flow and all-pole modelling of the vocal tract. The method provides a possibil- ity to retain and manipulate all relevant prosodic features of any kind of speech. Most importantly, the features include voice quality, which has not been properly modeled in earlier delex- icalization methods. The functionality of the new method was tested in a prosodic tagging experiment aimed at providing word prominence data for a text-to-speech synthesis system. The ex- periment confirmed the usefulness of the method and further corroborated earlier evidence that linguistic factors influence the perception of prosodic prominence.