Estratégias de indeterminação do sujeito no discurso especializado Web-Mediated

ABSTRACTReading and conducting research can initially feel daunting for community college students. For this nontraditional aged, first generation student-researcher, the interest in research increased with the availability of TEDx Talks. The overall intent of the study was to evaluate the empathetic reactivity of students (n = 78) while viewing a prosocial behavior TEDx Talk. Students completed the Interpersonal Reactivity Index (IRI) pre/post-assessment to understand if empathy levels changed from before and after viewing the video. Results showed statistically significant changes for both males and females suggesting increased levels of empathy after viewing the video. For the student-researcher, certitude was found as data were analyzed; initial positive feelings about learning research utilizing TEDx Talks were confirmed with statistical analysis, thereby strengthening confidence that learning was not only hypothesized but secured by empirical evidence.

Using an interpretive thematic analysis of two American and two Filipino Millennial teachers’ TED Talk and TEDx Talks online video files, this paper aimed to explore the values, beliefs, and worldviews underlying their apparently counter-culture decision to teach in disadvantaged public schools in high poverty rate areas. Through a contextualist lens and using Lloyd Kwast’s model of culture, a cross-cultural comparison of the cultural components of their decisions revealed through their speeches revealed subtle yet fundamental intra-group similarities and differences. Analysis revealed that young American teachers’ values revolve around equity and justice while the Filipinos’ were on children and community welfare. The Americans beliefs centered on the potency of socio-economic opportunities and attitude while the Filipinos believed in the capacity sincere contribution and the highlighting of positive aspects to get things done. Despite these, there appears to be hints of similarities between these two groups until this point. The fundamental difference was revealed in their worldviews. The American worldview was based on the idea and vision of the Enlightenment notions that founded their nation. The Filipinos’ worldviews were based on living with and consequently finding themselves in others. Underneath the counter-culture decision to teach in disadvantaged schools, lies the fundamental cultural differences consisting of rich latent networks and motivations unique to the society and context where they thrive.

We study the individuality of the human voice with respect to a widely used feature representation of speech utterances, namely, the i-vector model. As a first step toward this goal, we compare and contrast uniqueness measures proposed for different biometric modalities. Then, we introduce a new uniqueness measure that evaluates the entropy of i-vectors while taking into account speaker level variations. Our measure operates in the discrete feature space and relies on accurate estimation of the distribution of i-vectors. Therefore, i-vectors are quantized while ensuring that both the quantized and original representations yield similar speaker verification performance. Uniqueness estimates are obtained from two newly generated datasets and the public VoxCeleb dataset. The first custom dataset contains more than one and a half million speech samples of 20,741 speakers obtained from TEDx Talks videos. The second one includes over twenty one thousand speech samples from 1,595 actors that are extracted from movie dialogues. Using this data, we analyzed how several factors, such as the number of speakers, number of samples per speaker, sample durations, and diversity of utterances affect uniqueness estimates. Most notably, we determine that the discretization of i-vectors does not cause a reduction in speaker recognition performance. Our results show that the degree of distinctiveness offered by i-vector-based representation may reach 43–70 bits considering 5-second long speech samples; however, under less constrained variations in speech, uniqueness estimates are found to reduce by around 30 bits. We also find that doubling the sample duration increases the distinctiveness of the i-vector representation by around 20 bits.