The Future of Feminist Theory: Dreams for New Knowledges

Dado que o feminismo não conseguiu um dos seus objetivosfundamentais, a criação de uma profunda igualdade ou a constituição de uma verdadeira e autônoma prática, na qual as mulheres escolhem por si as definições de si mesmas e do seu mundo, ideal representado nas filosofias da diferença sexual -- agora pode ser tempo de, mais uma vez retomar a questão. Não no intuito de prever o que será a teoria feminista no futuro, mas de investigação do que poderia ser, talvez até mesmo o que deveria ser. A preocupação não é extrapolar a teoria feminista como a conhecemos hoje. Em vez de perguntar: o que a teoria feminista pode se tornar no futuro? Como ela vai mudar? Como continuará a mesma? Ou seja, em vez de prever o que pode ocorrer com a teoria feminista, aquiquero discutir algo que parece muito próximo, mas é realmente muito diferente: a questão do que a teoria feminista deve ser, os meus anseios de um pensamento feminista futuro. O que é a teoria feminista no seu melhor?Qual é a sua contínua promessa radical? Como ela está localizada em relação a outros saberes? O que pode aspirar a teoria feminista? Como podemos construir saberes, técnicas, métodos e práticas que produzam novos tipos de sujeitos e novas relações sociais

Féminisme, matérialisme et liberté

In the past few years, the “new materialisms”, which draw particularly on anthropology and the history of science, have abounded in feminist and queer thought. This does not mean that their arguments coincide, however. Elizabeth Grosz’s argumentation, defined as a “materialist ontology”, tries to reintroduce “what it is in the nature of bodies in biological evolution that opens them up to cultural transcription, social immersion and production, that is, to political cultural and conceptual ev..

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Acoustic emphasis in four year olds

Acoustic emphasis may convey a range of subtle discourse distinctions, yet little is known about how this complex ability develops in children. This paper presents a first investigation of the factors which influence the production of acoustic prominence in young children’s spontaneous speech. In a production experiment, SVO sentences were elicited from 4 year olds who were asked to describe events in a video. Children were found to place more acoustic prominence both on ‘new’ words and on words that were ‘given’ but had shifted to a more accessible position within the discourse. This effect of accessibility concurs with recent studies of adult speech. We conclude that, by age four, children show appropriate, adult-like use of acoustic prominence, suggesting sensitivity to a variety of discourse distinctions

Dimensions of Time

Humanities Research Group Working Papers 8 Time: whether we’ve too much time on our hands or no time to stop and think, whether time flies or marches slowly, whether we are clock watchers or don’t own a watch, perennially late or inevitably early, the nature of time preoccupies us all. It is fitting, then, that this volume, the last to appear in the century beginning with “nineteen,” should take as its theme “Dimensions of Time.” This volume, as have all in the series, examines a topic of contemporary interest from a variety of historical and disciplinary perspectives.https://scholar.uwindsor.ca/hrg-working-papers/1007/thumbnail.jp

Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech

We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as Statement, Question, Backchannel, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling changed