Segment Boundary Detection via Class Entropy Measurements in Connectionist Phoneme Recognition

This article investigates the possibility to use the class entropy of the output of a connectionist phoneme recogniser to predict time boundaries between phonetic classes. The rationale is that the value of the entropy should increase in proximity of a transition between two segments that are well modelled (known) by the recognition network since it is a measure of uncertainty. The advantage of this measure is its simplicity as the posterior probabilities of each class are available in connectionist phoneme recognition. The entropy and a number of measures based on differentiation of the entropy are used in isolation and in combination. The decision methods for predicting the boundaries range from simple thresholds to neural network based procedure. The different methods are compared with respect to their precision, measured in terms of the ratio between the number C of predicted boundaries within 10 or 20 msec of the reference and the total number of predicted boundaries, and recall, measured as the ratio between C and the total number of reference boundaries

Self-Supervised Vision-Based Detection of the Active Speaker as Support for Socially-Aware Language Acquisition

This paper presents a self-supervised method for visual detection of the active speaker in a multi-person spoken interaction scenario. Active speaker detection is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. The proposed method is intended to complement the acoustic detection of the active speaker, thus improving the system robustness in noisy conditions. The method can detect an arbitrary number of possibly overlapping active speakers based exclusively on visual information about their face. Furthermore, the method does not rely on external annotations, thus complying with cognitive development. Instead, the method uses information from the auditory modality to support learning in the visual domain. This paper reports an extensive evaluation of the proposed method using a large multi-person face-to-face interaction dataset. The results show good performance in a speaker dependent setting. However, in a speaker independent setting the proposed method yields a significantly lower performance. We believe that the proposed method represents an essential component of any artificial cognitive system or robotic platform engaging in social interactions.Comment: 10 pages, IEEE Transactions on Cognitive and Developmental System

Interactive Robot Learning of Gestures, Language and Affordances

A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements of the environment (affordance exploration) with the probabilistic observation of another agent's actions. We propose a model that unites (i) learning robot affordances and word descriptions with (ii) statistical recognition of human gestures with vision sensors. We discuss theoretical motivations, possible implementations, and we show initial results which highlight that, after having acquired knowledge of its surrounding environment, a humanoid robot can generalize this knowledge to the case when it observes another agent (human partner) performing the same motor actions previously executed during training.Comment: code available at https://github.com/gsaponaro/glu-gesture

Active Mini-Batch Sampling using Repulsive Point Processes

The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches. We explore sampling schemes where similar data points are less likely to be selected in the same mini-batch. In particular, we prove that such repulsive sampling schemes lowers the variance of the gradient estimator. This generalizes recent work on using Determinantal Point Processes (DPPs) for mini-batch diversification (Zhang et al., 2017) to the broader class of repulsive point processes. We first show that the phenomenon of variance reduction by diversified sampling generalizes in particular to non-stationary point processes. We then show that other point processes may be computationally much more efficient than DPPs. In particular, we propose and investigate Poisson Disk sampling---frequently encountered in the computer graphics community---for this task. We show empirically that our approach improves over standard SGD both in terms of convergence speed as well as final model performance

S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction

We address the video prediction task by putting forth a novel model that combines (i) our recently proposed hierarchical residual vector quantized variational autoencoder (HR-VQVAE), and (ii) a novel spatiotemporal PixelCNN (ST-PixelCNN). We refer to this approach as a sequential hierarchical residual learning vector quantized variational autoencoder (S-HR-VQVAE). By leveraging the intrinsic capabilities of HR-VQVAE at modeling still images with a parsimonious representation, combined with the ST-PixelCNN's ability at handling spatiotemporal information, S-HR-VQVAE can better deal with chief challenges in video prediction. These include learning spatiotemporal information, handling high dimensional data, combating blurry prediction, and implicit modeling of physical characteristics. Extensive experimental results on the KTH Human Action and Moving-MNIST tasks demonstrate that our model compares favorably against top video prediction techniques both in quantitative and qualitative evaluations despite a much smaller model size. Finally, we boost S-HR-VQVAE by proposing a novel training method to jointly estimate the HR-VQVAE and ST-PixelCNN parameters.Comment: 14 pages, 7 figures, 3 tables. Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence on 2023-07-1

Cluster Analysis of Differential Spectral Envelopes on Emotional Speech

This paper reports on the analysis of the spectral variation of emotional speech. Spectral envelopes of time aligned speech frames are compared between emotionally neutral and active utterances. Statistics are computed over the resulting differential spectral envelopes for each phoneme. Finally, these statistics are classified using agglomerative hierarchical clustering and a measure of dissimilarity between statistical distributions and the resulting clusters are analysed. The results show that there are systematic changes in spectral envelopes when going from neutral to sad or happy speech, and those changes depend on the valence of the emotional content (negative, positive) as well as on the phonetic properties of the sounds such as voicing and place of articulation

Analisi gerarchica degli inviluppi spettrali differenziali di una voce emotiva

.In questo articolo viene descritto un nuovo metodo di analisi del timbro vocale tramite lo studio delle variazioni di inviluppo spettrale utilizzato da uno stesso parlatore in situazioni emotiva neutra o espressiva. Il contesto dell\u27analisi riguarda un corpus di un solo parlatore istruito a leggere una serie di frasi utilizzando uno stile di lettura neutro e successivamente utilizzando due modalit? emotive: uno stile allegro e uno stile triste. Gli inviluppi spettrali relativi alle versioni allineate delle realizzazioni vocali neutre e espressive (allegra e triste) sono confrontati utilizzando un metodo differenziale. Le differenze sono state calcolate tra lo stato emotivo e quello neutro, di conseguenza le due categorie messe a confronto sono neutro-allegro e neutro-triste. La statistica degli inviluppi differenziali ? stata calcolata per ogni fono. I dati sono stati esaminati utilizzando un metodo di clustering gerarchico di tipo agglomerativo. I cluster risultanti sono avvalorati con diverse misure di distanza tra le distribuzioni statistiche ed esplorati visivamente per trovare similitudini e differenze tra le due categorie. I risultati mettono in evidenza sistematiche variazioni nel timbro vocale relative ai due insiemi di differenze di inviluppi spettrali. Questi tratti dipendono dalla valenza dell\u27emozione presa in considerazione (positiva, negativa) come dalle propriet? fonetiche del particolare fono come ad esempio sonorit? e luogo di articolazione

User Evaluation of the SYNFACE Talking Head Telephone

Abstract. The talking-head telephone, Synface, is a lip-reading support for people with hearing-impairment. It has been tested by 49 users with varying degrees of hearing-impaired in UK and Sweden in lab and home environments. Synface was found to give support to the users, especially in perceiving numbers and addresses and an enjoyable way to communicate. A majority deemed Synface to be a useful product.