The role of speech technology in user perception and context acquisition in HRI

The role and relevance of speech synthesis and speech recognition in social robotics is addressed in this paper. To increase the generality of this study, the interaction of a human being with one and two robots when executing tasks was considered. By making use of these scenarios, a state-of-the-art speech synthesizer was compared with non-linguistic utterances (1) from the human preference and (2) perception of the robots' capabilities, (3) speech recognition was compared with typed text to input commands regarding the user preference, and (4) the importance of knowing the context of robots and (5) the role of synthetic voice to acquire this context were evaluated. Speech synthesis and recognition are different technologies but generating and understanding speech should be understood as different dimensions of the same spoken language phenomenon. Also, robot context denotes all the information about operating conditions and completeness status of the task that is being executed by the robot. Two robotic setups for online experiments were built. With the first setup, where only one robot was employed, our findings indicate that: highly natural synthetic speech is preferred over beep-like audio; users also prefer to enter commands by voice rather than by typing text; and, the robot voice has a more important effect on the perceived robot's capability than the possibility to input commands by voice. The analysis presented here suggests that when the users interacted with a single robot, its voice as a social cue and cause of anthropomorphization lost relevance while the interaction was carried out and the users could evaluate better the robot's capability with respect to its task. In the experiment with the second setup, a two-robot collaborative testbed was employed. When the robots communicated to each other to sort out the problems while they were trying to accomplish a mission, the user observed the situation from a more distanced position and the "reflective" perspective dominated. Our results indicate that to acquire the robots' context was perceived as essential for a successful human-robot collaboration to accomplish a given objective. For this purpose, synthesized speech was preferred over text on a screen for context acquisition.Comisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 1151306 ONRG 62909-17-1-200

Automatic detection of volcano-seismic events by modeling state and event duration in hidden Markov models

In this paper we propose an automatic volcano event detection system based on Hidden Markov Model (HMM) with state and event duration models. Since different volcanic events have different durations, therefore the state and whole event durations learnt from the training data are enforced on the corresponding state and event duration modes within-the HMM. Seismic signals from the Llaima volcano are used to train the system. Two types of events are employed in this study, Long Period (LP) and Volcano-Tectonic (VT). Experiments show that the standard HMMs can detect the volcano events with high accuracy but generates false positives. The results presented in this paper show that the incorporation of duration modeling can lead to reductions in false positive rate in event detection as high as 31% with a true positive accuracy equal to 94%. Further evaluation of the false positives indicate that the false alarms generated by the system were mostly potential events based on the signal-to-noise ratio criteria recommended by a volcano expert.Chilean National Commission for Scientific and Technological Research (CONICYT), PIA, Anillo project ACT-1120 FONDEF IDeA CA13I10273 OVDA

An unsupervised Hidden Markov Model-based system for the detection and classification of blue whale vocalizations off Chile

This paper analyzes the procedure used by FIFA up until 2018 to rank national football teams and define by random draw the groups for the initial phase of the World Cup finals. A predictive model is calibrated to form a reference ranking to evaluate the performance of a series of simple changes to that procedure. These proposed modifications are guided by a qualitative and statistical analysis of the FIFA ranking. We then analyze the use of this ranking to determine the groups for the World Cup finals. After enumerating a series of deficiencies in the group assignments for the 2014 World Cup, a mixed integer linear programming model is developed and used to balance the difficulty levels of the groups

Implantable neuroamplifers for electrocorticography using flexible and biocompatible technology

Brain signals such as electroencephalography (EEG) and electrocorticography (ECoG) are used to diagnose epilepsy. ECoG signals are small and therefore require large amplification while keeping the recording electronics small enough to adapt to the surface of the brain. Moreover, the components have to be of low power to reduce the risk of brain damage while recording the brain. Herein, a neuroamplifier that is integrated in an ECoG is described. The amplifier, in combination with a novel multiplexing system that reduces the number of required amplifiers and ensures the flexibility of the ECoG, achieves the desired signal-to-noise ratio while reducing power consumption. The feasibility of the proposed design is validated though electronic simulations for different input signals, analyzing the actual amplification achieved and the response times. Moreover the circuit is implemented and real measurements are provided validating the simulations.IHeaR LAT_Struc-133 01DN1704