This paper defines the notion of analogical dissimilarity between four objects, with a special focus on objects structured as sequences. Firstly, it studies the case where the four objects have a null analogical dissimilarity, i.e. are in analogical proportion. Secondly, when one of these objects is unknown, it gives algorithms to compute it. Thirdly, it tackles the problem of defining analogical dissimilarity, which is a measure of how far four objects are from being in analogical proportion. In particular, when objects are sequences, it gives a definition and an algorithm based on an optimal alignment of the four sequences. It gives also learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical dissimilarity with a given object. Two practical experiments are described: the first is a classification problem on benchmarks of binary and nominal data, the second shows how the generation of sequences by solving analogical equations enables a handwritten character recognition system to rapidly be adapted to a new writer.

Linguistic corpus design is a critical concern for building rich annotated corpora useful in different domains of applications. For example, speech technologies such as ASR (Automatic Speech Recognition) or TTS (Text-to-Speech) need a huge amount of speech data to train data-driven models or to produce synthetic speech. Collecting data is always related to costs (recording speech, verifying annotations, etc.), and as a rule of thumb, the more data you gather, the more costly your application will be. Within this context, we present in this article solutions to reduce the amount of linguistic text content while maintaining a sufficient level of linguistic richness required by a model or an application. This problem can be formalized as a Set Covering Problem (SCP) and we evaluate two algorithmic heuristics applied to design large text corpora in English and French for covering phonological information or POS labels. The first considered algorithm is a standard greedy solution with an agglomerative/spitting strategy and we propose a second algorithm based on Lagrangian relaxation. The latter approach provides a lower bound to the cost of each covering solution. This lower bound can be used as a metric to evaluate the quality of a reduced corpus whatever the algorithm applied. Experiments show that a suboptimal algorithm like a greedy algorithm achieves good results; the cost of its solutions is not so far from the lower bound (about 4.35% for 3-phoneme coverings). Usually, constraints in SCP are binary; we proposed here a generalization where the constraints on each covering feature can be multi-valued.

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This paper presents an attempt to evaluate three different sets of features extracted from prosodic descriptors and Big Five traits for building an anomaly detector. The Big Five model enables to capture personality information. Big Five traits are extracted from a manual annotation while Prosodic features are extracted directly from the speech signal. Two different anomaly detection methods are evaluated: Gaussian Mixture Model (GMM) and One-Class SVM (OC-SVM), each one combined with a threshold classification to decide the "normality" of a sample. The different combinations of models and feature sets are evaluated on the SSPNET-Personality corpus which has already been used in several experiments, including a previous work on separating two types of personality profiles in a supervised way. In this work, we propose the above mentioned unsupervised or semi-supervised methods, and discuss their performance, to detect particular audio-clips produced by a speaker with an abnormal personality. Results show that using automatically extracted prosodic features competes with the Big Five traits. The overall detection performance achieved by the best model is around 0.8 (F1-measure).