This thesis explores the temporal analysis of text using the implicit temporal cues present in document. We consider the case when all explicit temporal expressions such as specific dates or years are removed from the text and a bag of words based approach is used for timestamp prediction for the text. A set of gold standard text documents with times- tamps are used as the training set. We also predict time spans for Wikipedia biographies based on their text. We have training texts from 3800 BC to present day. We partition this timeline into equal sized chronons and build a probability histogram for a test document over this chronon sequence. The document is assigned to the chronon with the highest probability. We use 2 approaches: 1) a generative language model with Bayesian priors, and 2) a KL divergence based model. To counter the sparsity in the documents and chronons we use 3 different smoothing techniques across models. We use 3 diverse datasets to test our mod- els: 1) Wikipedia Biographies, 2) Guttenberg Short Stories, and 3) Wikipedia Years dataset. Our models are trained on a subset of Wikipedia biographies. We concentrate on two prediction tasks: 1) time-stamp prediction for a generic text or mid-span prediction for a Wikipedia biography , and 2) life-span prediction for a Wikipedia biography. We achieve an f-score of 81.1% for life-span prediction task and a mean error of around 36 years for mid-span prediction for biographies from present day to 3800 BC. The best model gives a mean error of 18 years for publication date prediction for short stories that are uniformly distributed in the range 1700 AD to 2010 AD. Our models exploit the temporal distribu- tion of text for associating time. Our error analysis reveals interesting properties about the models and datasets used. We try to combine explicit temporal cues extracted from the document with its implicit cues and obtain combined prediction model. We show that a combination of the date-based predictions and language model divergence predictions is highly effective for this task: our best model obtains an f-score of 81.1% and the median error between actual and predicted life span midpoints is 6 years. This would be one of the emphasis for our future work. The above analyses demonstrates that there are strong temporal cues within texts that can be exploited statistically for temporal predictions. We also create good benchmark datasets along the way for the research community to further explore this problem. / text

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Cette thèse vise à caractériser le traitement auditif des informations spectro-temporelles impliquées dans la perception de la parole au cours du développement précoce. Dans ce but, les capacités de discrimination de contrastes phonétiques sont évaluées à l’aide de deux méthodes comportementales chez des enfants âgés de 6 et 10 mois. Les sons de parole sont dégradés par des « vocodeurs » conçus pour réduire sélectivement les modulations spectrales et/ou temporelles des stimuli phonétiquement contrastés.Les trois premières études de cette thèse montrent que les informations spectro-temporelles fines de la parole (les indices de modulation de fréquence et détails spectraux) ne sont pas nécessaires aux enfants français de 6 mois pour percevoir le trait phonétique de voisement et de lieu d’articulation. Comme pour les adultes français, les informations de modulation d’amplitude les plus lentes semblent suffire pour percevoir ces traits phonétiques. Les deux dernières études montrent cependant que les informations spectro-temporelles fines sont requises pour la discrimination de tons lexicaux (variations de hauteur liée au sens de mots monosyllabiques) chez les enfants français et taiwanais de 6 mois. De plus, ces études montrent l’influence de l’expérience linguistique sur le poids perceptif de ces informations de modulations dans la discrimination de la parole chez les jeunes adultes et les enfants français et taiwanais de 10 mois.Ces études montrent que les mécanismes auditifs spectro-temporels sous-tendant la perception de la parole sont efficaces dès l’âge de 6 mois, mais que ceux-ci vont être influencés par l’exposition à l’environnement linguistique dans les mois suivants. Enfin, cette thèse discute les implications de ces résultats vis-à-vis de l’implantation précoce des enfants sourds profonds qui reçoivent des informations de modulations dégradées. / The goal of this doctoral research was to characterize the auditory processing of the spectro-temporal cues involved in speech perception during development. The ability to discriminate phonetic contrasts was evaluated in 6- and 10-month-old infants using two behavioral methods. The speech sounds were processed by “vocoders” designed to reduce selectively the spectro-temporal modulation content of the phonetically contrasting stimuli. The first three studies showed that fine spectro-temporal modulation cues (the frequency-modulation cues and spectral details) are not required for the discrimination of voicing and place of articulation in French-learning 6-month-old infants. As for French adults, 6-month-old infants can discriminate those phonetic features on the sole basis of the slowest amplitude-modulation cues. The last two studies revealed that the fine modulation cues are required for lexical-tone (pitch variations related to the meaning of one-syllable word) discrimination in French- and Mandarin-learning 6-month-old infants. Furthermore, the results showed the influence of linguistic experience on the perceptual weight of these modulation cues in both young adults and 10-month-old infants learning either French or Mandarin.This doctoral research showed that the spectro-temporal auditory mechanisms involved in speech perception are efficient at 6 months of age, but will be influenced by the linguistic environment during the following months. Finally, the present research discusses the implications of these findings for cochlear implantation in profoundly deaf infants who have only access to impoverished speech modulation cues.