Joan Claudi Socoró

Towards High-Quality Next-Generation Text-to-Speech Synthesis: A Multidomain Approach by Automatic Domain Classification

This paper is a contribution to the recent advancements in the development of high-quality next generation text-to-speech (TTS) synthesis systems. Two of the hottest research topics in this area are oriented towards the improvement of speech expressiveness and flexibility of synthesis. In this context, this paper presents a new TTS strategy called multidomain TTS (MD-TTS) for synthesizing among different domains. Although the multidomain philosophy has been widely applied in spoken language systems, few research efforts have been conducted to extend it to the TTS field. To do so, several proposals are described in this paper. First, a text classifier (TC) is included in the classic TTS architecture in order to automatically conduct the selection of the most appropriate domain for synthesizing the input text. In contrast to classic topic text classification tasks, the MD-TTS TC should not only consider the contents of text but also its structure. To this end, this paper introduces a new text modeling scheme based on an associative relational network, which represents texts as a directional weighted word-based graph. The conducted experiments validate the proposal in terms of both objective (TC efficiency) and subjective (perceived synthetic speech quality) evaluation criteria.

Morphological analysis of mammary biopsy images

The authors present an automatic system to analyse a cell nucleus in a given biopsy of mammary tissue which is cancerous. Identification and characterization of the cell nucleus provides enough information to diagnose a high or low grade cancer. This is a high interest differentiation for the pathologist to apply the correct therapy. Performing these measures by human observation is a hard, imprecise and subjective task. The algorithm they present processes the image in order to identify the cell nucleus over the rest of the tissue. Images are enhanced and segmented using morphological transformations. An ultimate erosion is used in two steps to separate cell nuclei in contact. It is based in a combination of symmetrical ultimate erosion with directional ultimate erosion.

Driving assistance system based on the detection of head-on collisions

An artificial vision system for vehicles is proposed in this article to alert drivers of potential head on collisions. It is capable of detecting any type of frontal collision from any type of obstacle that may present itself in a vehiclepsilas path. The system operates based on a sequence of algorithms whose images are recorded on a camera located in the moving vehicle, resulting in the calculation of Time-to-Contact taken from an analysis of the optical flow, which allows the vehiclepsilas movement to be studied from a sequence of images.

Feature diversity in cluster ensembles for robust document clustering

The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based on feature diversity and cluster ensembles is presented in this work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model order selection and able to detect constructive interactions between different document representations in the test bed.

Positional and confidence voting-based consensus functions for fuzzy cluster ensembles

Consensus clustering, i.e. the task of combining the outcomes of several clustering systems into a single partition, has lately attracted the attention of researchers in the unsupervised classification field, as it allows the creation of clustering committees that can be applied with multiple interesting purposes, such as knowledge reuse or distributed clustering. However, little attention has been paid to the development of algorithms, known as consensus functions, especially designed for consolidating the outcomes of multiple fuzzy (or soft) clustering systems into a single fuzzy partition-despite the fact that fuzzy clustering is far more informative than its crisp counterpart, as it provides information regarding the degree of association between objects and clusters that can be helpful for deriving richer descriptive data models. For this reason, this paper presents a set of fuzzy consensus functions capable of creating soft consensus partitions by fusing a collection of fuzzy clusterings. Our proposals base clustering combination on a cluster disambiguation process followed by the application of positional and confidence voting techniques. The modular design of these algorithms makes it possible to sequence their constituting steps in different manners, which allows to derive versions of the proposed consensus functions optimized from a computational standpoint. The proposed consensus functions have been evaluated in terms of the quality of the consensus partitions they deliver and in terms of their running time on multiple benchmark data sets. A comparison against several representative state-of-the-art consensus functions reveals that our proposals constitute an appealing alternative for conducting fuzzy consensus clustering, as they are capable of yielding high quality consensus partitions at a low computational cost.

DYNAMAP – Development of low cost sensors networks for real time noise mapping

Abstract The Environmental Noise Directive (END) requires that regular updating of noise maps is implemented every five years to check and report about the changes occurred during the reference period. The updating process is usually achieved using a standardized approach, consisting in collating and processing information through acoustic models to produce the updated maps. This procedure is time consuming and costly, and has a significant impact on the budget of the authorities responsible for providing the maps. Furthermore, END requires that simplified and easy-to-read noise maps are made available to inform the public about noise levels and actions to be undertaken by local and central authorities to reduce noise impacts. To make the updating of noisemaps easier and more cost effective, there is a need for integrated systems that incorporate real-time measurement and processing to assess the acoustic impact of noise sources. To that end, a dedicated project, named DYNAMAP (DYNamic Acoustic MAPping), has been proposed and co-financed in the framework of the LIFE 2013 program, with the aim to develop a dynamic noise mapping system able to detect and represent in real time the acoustic impact of road infrastructures. In this paper, after a comprehensive description of the project idea, objectives and expected results, the most important steps to achieve the ultimate goal are described.

Prosody Modelling of Spanish for Expressive Speech Synthesis

This paper presents the use of analogical learning, in particular case-based reasoning, for the automatic generation of prosody from text, which is automatically tagged with prosodic features. This is a corpus-based method for quantitative modelling of prosody to be used in a Spanish text to speech system. The main objective is the development of a method for predicting the three main prosodic parameters: the fundamental frequency (F0) contour, the segmental duration and energy. Both objective and subjective experiments have been conducted in order to evaluate the accuracy of our proposal.

BordaConsensus: a new consensus function for soft cluster ensembles

Consensus clustering is the task of deriving a single labeling by applying a consensus function on a cluster ensemble. This work introduces BordaConsensus, a new consensus function for soft cluster ensembles based on the Borda voting scheme. In contrast to classic, hard consensus functions that operate on labelings, our proposal considers cluster membership information, thus being able to tackle multiclass clustering problems. Initial small scale experiments reveal that, compared to state-of-the-art consensus functions, BordaConsensus constitutes a good performance vs. complexity trade-off.

Reliability in ICA-Based Text Classification

This paper introduces a novel approach for improving the reliability of ICA-based text classifiers, attempting to make the most of the independent components of the text data. In this framework, two issues are adressed: firstly, a relative relevance measure for category assignment is presented. And secondly, a reliability control process is included in the classifier, avoiding the classification of documents belonging to none of the categories defined during the training stage. The experiments have been conducted on a journalistic-style text corpus in Catalan, achieving encouraging results in terms of rejection accuracy. However, similar results are obtained when comparing the proposed relevance measure to the classic magnitude-based technique for category assignment.

ICA-based hierarchical text classification for multi-domain text-to-speech synthesis

In the framework of multi-domain text-to-speech synthesis, it is essential (i) to design a hierarchically structured database for allowing several domains in the same speech corpus and (ii) to include a text classification module that, at run time, assigns the input sentences to a domain or set of domains from the database. We present a hierarchical text classifier based on independent component analysis (ICA), which is capable of (i) organizing the contents of the corpus in a hierarchical manner and (ii) classifying the texts to be synthesized according to the learned structure. The document organization and classification performance of our ICA-based hierarchical classifier are evaluated in several encouraging experiments conducted on a journalistic-style text corpus for speech synthesis in Catalan.