Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

A Comparative Study of Wavelet Based Feature Extraction Techniques in Recognizing Isolated Spoken Words

Speech is a natural mode of communication for people and speech recognition is an intensive area of research due to its versatile applications. This paper presents a comparative study of various feature extraction methods based on wavelets for recognizing isolated spoken words. Isolated words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. This work includes two speech recognition methods. First one is a hybrid approach with Discrete Wavelet Transforms and Artificial Neural Networks and the second method uses a combination of Wavelet Packet Decomposition and Artificial Neural Networks. Features are extracted by using Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Training, testing and pattern recognition are performed using Artificial Neural Networks (ANN). The proposed method is implemented for 50 speakers uttering 20 isolated words each. The experimental results obtained show the efficiency of these techniques in recognizing speech

Departament de Psicologia [Ressenya del llibre Adolescents and audio-visual media in five countries, de Ferran Casas, Irene Rizzini, Rose September, Per Egil Mjaavatn i Usha Nayar (coord.)]

Aquest llibre és el producte d'anys de cooperació entre equips de recerca de cinc països diferents, tot ells Key Institutions de la xarxa Childwatch International, en el marc d'un projecte plurinacional sobre adolescents i mitjans

Confección del documental audio-visual histórico-etnográfico : los religiosos castellano-leoneses en la evangelización de América.

No está publicado.

Iris : proyecto audio-visual : base de datos e im??genes sobre personas con discapacidad

El v??deo est?? realizado por profesores de las ??reas de Did??ctica y Organizaci??n Escolar y Teor??a e Historia de la Educaci??n, durante los a??os 2000 y 2001. Recoge la opini??n de profesionales, padres y madres y personas con discapacidad f??sica (sordos, ciegos y P.C.I.)y personas con discapacidad mental en relaci??n con diferentes aspectos de la vida diaria: hogar, inserci??n laboral, etc. Este recurso did??ctico est?? dise??ado para el visionado, interpretaci??n te??rico-pr??ctica y contraste de opiniones en el aula, de ense??anza superior, para abordar la formaci??n de los profesionales que van a desarrollar su actividad con personas con discapacidad.

La metodología estructuro-global audio-visual y la enseñanza del francés a distancia.

La función de la Lengua en el Bachillerato es triple: como factor de promoción socio-económica que permite en algunos casos obtener mejoras salariales y en otros alcanzar puestos vedados a los que no conocen idiomas, la UNESCO recomienda su estudio por su función educativa respecto al ser humano, integrante de los distintos grupos nacionales, enriquecimiento del sentido crítico y de tolerancia al apreciar las diferencias y semejanzas de los distintos pueblos, una cultura humanista que debe procurar el estudio de la lengua francesa, máxime para nosotros si tenemos en cuenta que es un país fronterizo nuestro y que permite el camino para llegar a Europa, es lógico que la lengua francesa sea tan importante para nosotros debido a las relaciones comerciales, económicas, etcétera que se desarrollan en esta lengua.; como tercera función, y primordial, el apredizaje de, por lo menos, un idioma, es primordial para la formación de la personalidad. A partir de 1975 son importantes los avances conseguidos en el estudio de un idioma, sobre todo los esfuerzos de renovación didáctica, destacando las aportaciones de la metodología estructuroglobal audiovisual, nacida a partir de los años cincuenta y que está siendo renovada constantemente. Si el alumno ha de aprender el francés a distancia debe tener un material adecuado a través de cassettes con diálogos para aprender a pronunciar correctamente. Después se aprenderá a leer y escribir porque se supone que se sabe pronunciar correctamente y el transcribir la lengua oral es un ejercicio para fijar los conocimientos. Pero el aprendizaje de un idioma debe realizarse dedicando todos los días un tiempo concreto, esta regularidad es la permite aprenderlo. Así, en cada caso el alumno deberá actuar de acuerdo con las orientaciones más precisas y personales de su profesor-tutor y con sus hábitos de trabajo siempre y cuando resulten eficaces.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

Televised Whorf: cognitive restructuring in advanced foreign language learners as a function of audio-visual media exposure

The encoding of goal-oriented motion events varies across different languages. Speakers of languages without grammatical aspect (e.g., Swedish) tend to mention motion endpoints when describing events, e.g., “two nuns walk to a house,”, and attach importance to event endpoints when matching scenes from memory. Speakers of aspect languages (e.g., English), on the other hand, are more prone to direct attention to the ongoingness of motion events, which is reflected both in their event descriptions, e.g., “two nuns are walking.”, and in their non-verbal similarity judgements. This study examines to what extent native speakers of Swedish (n = 82) with English as a foreign language (FL) restructure their categorisation of goal-oriented motion as a function of their English proficiency and experience with the English language (e.g., exposure, learning). Seventeen monolingual native English speakers from the United Kingdom (UK) were engaged for comparison purposes. Data on motion event cognition were collected through a memory-based triads matching task, in which a target scene with an intermediate degree of endpoint orientation was matched with two alternative scenes with low and high degrees of endpoint orientation, respectively. Results showed that the preference among the Swedish speakers of L2 English to base their similarity judgements on ongoingness rather than event endpoints was correlated with their use of English in their everyday lives, such that those who often watched television in English approximated the ongoingness preference of the English native speakers. These findings suggest that event cognition patterns may be restructured through the exposure to FL audio-visual media. The results thus add to the emerging picture that learning a new language entails learning new ways of observing and reasoning about reality.

N1 enhancement in synesthesia during visual and audio-visual perception in semantic cross-modal conflict situations: an ERP study

Synesthesia entails a special kind of sensory perception, where stimulation in one sensory modality leads to an internally generated perceptual experience of another, not stimulated sensory modality. This phenomenon can be viewed as an abnormal multisensory integration process as here the synesthetic percept is aberrantly fused with the stimulated modality. Indeed, recent synesthesia research has focused on multimodal processing even outside of the specific synesthesia-inducing context and has revealed changed multimodal integration, thus suggesting perceptual alterations at a global level. Here, we focused on audio-visual processing in synesthesia using a semantic classification task in combination with visually or auditory-visually presented animated and in animated objects in an audio-visual congruent and incongruent manner. Fourteen subjects with auditory-visual and/or grapheme-color synesthesia and 14 control subjects participated in the experiment. During presentation of the stimuli, event-related potentials were recorded from 32 electrodes. The analysis of reaction times and error rates revealed no group differences with best performance for audio-visually congruent stimulation indicating the well-known multimodal facilitation effect. We found enhanced amplitude of the N1 component over occipital electrode sites for synesthetes compared to controls. The differences occurred irrespective of the experimental condition and therefore suggest a global influence on early sensory processing in synesthetes.

CENSUS HISTOGRAMS: A SIMPLE FEATURE EXTRACTION and MATCHING APPROACH FOR FACE RECOGNITION

Most face recognition approaches require a prior training where a given distribution of faces is assumed to further predict the identity of test faces. Such an approach may experience difficulty in identifying faces belonging to distributions different from the one provided during the training. A face recognition technique that performs well regardless of training is, therefore, interesting to consider as a basis of more sophisticated methods. In this work, the Census Transform is applied to describe the faces. Based on a scanning window which extracts local histograms of Census Features, we present a method that directly matches face samples. With this simple technique, 97.2% of the faces in the FERET fa/fb test were correctly recognized. Despite being an easy test set, we have found no other approaches in literature regarding straight comparisons of faces with such a performance. Also, a window for further improvement is presented. Among other techniques, we demonstrate how the use of SVMs over the Census Histogram representation can increase the recognition performance.