Visually grounded learning of keyword prediction from untranscribed speech

During language acquisition, infants have the benefit of visual cues to ground spoken language. Robots similarly have access to audio and visual sensors. Recent work has shown that images and spoken captions can be mapped into a meaningful common space, allowing images to be retrieved using speech and vice versa. In this setting of images paired with untranscribed spoken captions, we consider whether computer vision systems can be used to obtain textual labels for the speech. Concretely, we use an image-to-words multi-label visual classifier to tag images with soft textual labels, and then train a neural network to map from the speech to these soft targets. We show that the resulting speech system is able to predict which words occur in an utterance---acting as a spoken bag-of-words classifier---without seeing any parallel speech and text. We find that the model often confuses semantically related words, e.g. "man" and "person", making it even more effective as a semantic keyword spotter.Comment: 5 pages, 3 figures, 5 tables; small updates, added link to code; accepted to Interspeech 201

End-to-end Phoneme Sequence Recognition using Convolutional Neural Networks

Most phoneme recognition state-of-the-art systems rely on a classical neural network classifiers, fed with highly tuned features, such as MFCC or PLP features. Recent advances in ``deep learning'' approaches questioned such systems, but while some attempts were made with simpler features such as spectrograms, state-of-the-art systems still rely on MFCCs. This might be viewed as a kind of failure from deep learning approaches, which are often claimed to have the ability to train with raw signals, alleviating the need of hand-crafted features. In this paper, we investigate a convolutional neural network approach for raw speech signals. While convolutional architectures got tremendous success in computer vision or text processing, they seem to have been let down in the past recent years in the speech processing field. We show that it is possible to learn an end-to-end phoneme sequence classifier system directly from raw signal, with similar performance on the TIMIT and WSJ datasets than existing systems based on MFCC, questioning the need of complex hand-crafted features on large datasets.Comment: NIPS Deep Learning Workshop, 201

Pseudo-sluicing in Turkish: A pro-form Analysis

This study investigates pseudo-sluicing constructions in Turkish and argues that they can be best accounted for by a pro-form analysis. The explanation rests on the properties of pseudo-sluicing in Turkish such as lack of case connectivity, presence of copula in the pseudo-sluice, lack of island effect and the ungrammaticality with sprouting. All these characteristics significantly challenge a possible elliptical cleft approach, and provide evidence for a pro-form analysis where the wh-word is preceded by a null e-type pronoun, as originally suggested for sluicing-like constructions in Mandarin Chinese (cf. Adams 2004, Adams and Tomioka 2012)

Does defence spending impede economic growth? cointegration and causality analysis for Pakistan

This study revisits the relationship between defence spending and economic growth using Keynesian model in Pakistan by applying ARDL bounds testing approach to cointegration for long run and error correction method for short span of time. Empirical evidence suggests a stable cointegration relationship between defence spending and economic growth. An increase in defence spending retards the pace of economic growth confirming the validation of Keynesian hypothesis in the country. Current economic growth is positively linked with economic growth in previous period while rise in nonmilitary expenditures boosts economic growth. Interest rate is inversely associated with economic growth. Finally, unidirectional causality running from military spending to economic growth is found.Defence Spending, Economic Growth, Cointegration, Causality, Pakistan

Influencia de la intensidad de la caída del racimo sobre los compuestos bioactivos y la composición de ácidos grasos en la avellana

This study was conducted to determine how the intensity of the cluster drop effects nut traits, bioactive compounds, and fatty acid composition in Tombul, Palaz and Kalınkara hazelnut cultivars. The cluster drop significantly affected bioactive compounds and fatty acid composition while it did not affect the traits of the nuts. As cluster drop intensity increased, nut traits and bioactive compounds in all cultivars increased. Strong cluster drop intensity determined the highest total phenolics, total flavonoids, and antioxidant activity. Except for the Kalınkara cultivar, a low amount of linoleic acid was detected while high amounts of oleic and stearic acid were determined in slight cluster drop intensity. As cluster drop intensity increased, palmitic acid increased. Principal component analysis showed that the slight and intermediate drop intensity were generally associated with kernel length, oleic, linoleic, stearic, palmitoleic, 11-eicosenoic and arachidic acids. In contrast, strong intensity was associated with nut and kernel weight, kernel ratio, kernel width, kernel thickness, kernel size, bioactive compounds, and palmitic acid. As a result, the bioactive compounds and fatty acid composition, which are important for human health, was significantly affected by cluster drop intensity.El estudio se realizó para determinar el efecto de la intensidad de la caída de los racimos en las características de las avellanas, los compuestos bioactivos y la composición de ácidos grasos en cultivares de avellanas Tombul, Palaz y Kalınkara. La caída del racimo afectó significativamente a la composición de bioactivos y ácidos grasos, mientras que no afectó a las características de la avellana. A medida que aumentaba la intensidad de la caída de los racimos, aumentaban los compuestos bioactivos en todos los cultivares. La fuerte intensidad de caída de los racimos determinó que los fenoles totales, los flavonoides totales y la actividad antioxidante fueran más altos. Excepto para el cultivar Kalınkara, con un bajo contenido de ácido linoleico, un alto contenido de los ácidos oleico y esteárico se determinó en una ligera intensidad de caída de racimos. A medida que aumentaba la intensidad de la caída de los racimos, aumentaba el ácido palmítico. El análisis de componentes principales mostró que la intensidad de caída leve e intermedia generalmente se agrupaba con la longitud del grano, los ácidos oleico, linoleico, esteárico, palmitoleico, 11-eicosenoico y araquídico. En contraste, la intensidad fuerte se agrupó con el peso de la avellana y el grano, la proporción del grano, el ancho del grano, el grosor del grano, el tamaño del grano, los compuestos bioactivos y el ácido palmítico. Como resultado, la composición de compuestos bioactivos y ácidos grasos, que es eficaz para la salud humana, se vio significativamente afectada por la intensidad de la caída del grupo