Robust Speech Detection for Noisy Environments

This paper presents a robust voice activity detector (VAD) based on hidden Markov models (HMM) to improve speech recognition systems in stationary and non-stationary noise environments: inside motor vehicles (like cars or planes) or inside buildings close to high traffic places (like in a control tower for air traffic control (ATC)). In these environments, there is a high stationary noise level caused by vehicle motors and additionally, there could be people speaking at certain distance from the main speaker producing non-stationary noise. The VAD presented in this paper is characterized by a new front-end and a noise level adaptation process that increases significantly the VAD robustness for different signal to noise ratios (SNRs). The feature vector used by the VAD includes the most relevant Mel Frequency Cepstral Coefficients (MFCC), normalized log energy and delta log energy. The proposed VAD has been evaluated and compared to other well-known VADs using three databases containing different noise conditions: speech in clean environments (SNRs mayor que 20 dB), speech recorded in stationary noise environments (inside or close to motor vehicles), and finally, speech in non stationary environments (including noise from bars, television and far-field speakers). In the three cases, the detection error obtained with the proposed VAD is the lowest for all SNRs compared to Acero¿s VAD (reference of this work) and other well-known VADs like AMR, AURORA or G729 annex b

Estudio de diversos índices de fragilidad en enfermos mayores de 60 años ingresados en un servicio de medicina interna: prevalencia, relación con el estado de nutrición y valor pronóstico.

Con el aumento de la esperanza de vida cada vez asistimos a pacientes mayores y pluripatológicos, que a menudo padecen sarcopenia y fragilidad. Ambos conceptos se asocian entre sí y con otros aspectos de los ancianos como el deterioro cognitivo, la comorbilidad, la dependencia y la desnutrición. Existe una elevada prevalencia de desnutrición, dinapenia, sarcopenia, incapacidad y fragilidad en los pacientes ingresados y todos estos aspectos se relaciona con una mayor mortalidad al ingreso y en el seguimiento a largo plazo. La función muscular o dinapenia es el eje sobre el que pivotan la fragilidad y sarcopenia

Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector. Computers & Electrical Engineering (CAEE).

Nowadays, several computational techniques for speech recognition have been proposed. These techniques suppose an important improvement in real time applications where speaker interacts with speech recognition systems. Although researchers proposed many methods, none of them solve the high false alarm problem when far-field speakers interfere in a human-machine conversation. This paper presents a two-class (speech and non-speech classes) decision-tree based approach for combining new speech pulse features in a VAD (Voice Activity Detector) for rejecting far-field speech in speech recognition systems. This Decision Tree is applied over the speech pulses obtained by a baseline VAD composed of a frame feature extractor, a HMM-based (Hidden Markov Model) segmentation module and a pulse detector. The paper also presents a detailed analysis of a great amount of features for discriminating between close and far-field speech. The detection error obtained with the proposed VAD is the lowest compared to other well-known VAD

Improving automatic detection of obstructive sleep apnea through nonlinear analysis of sustained speech

We present a novel approach for the detection of severe obstructive sleep apnea (OSA) based on patients' voices introducing nonlinear measures to describe sustained speech dynamics. Nonlinear features were combined with state-of-the-art speech recognition systems using statistical modeling techniques (Gaussian mixture models, GMMs) over cepstral parameterization (MFCC) for both continuous and sustained speech. Tests were performed on a database including speech records from both severe OSA and control speakers. A 10 % relative reduction in classification error was obtained for sustained speech when combining MFCC-GMM and nonlinear features, and 33 % when fusing nonlinear features with both sustained and continuous MFCC-GMM. Accuracy reached 88.5 % allowing the system to be used in OSA early detection. Tests showed that nonlinear features and MFCCs are lightly correlated on sustained speech, but uncorrelated on continuous speech. Results also suggest the existence of nonlinear effects in OSA patients' voices, which should be found in continuous speech

Exploring differences between phonetic classes in Sleep Apnoea Syndrome Patients using automatic speech processing techniques

This work is part of an on-going collaborative project between the medical and signal processing communities to promote new research efforts on automatic OSA (Obstructive Apnea Syndrome) diagnosis. In this paper, we explore the differences noted in phonetic classes (interphoneme) across groups (control/apnoea) and analyze their utility for OSA detectio

Learning Gains in Higher Middle Education

We present the results of the first national study on learning gains in Mexico related to the experiences of three generations of students who complete their higher middle education as of 2013 and until 2015 We used data from standardized third grade ENLACE tests from secondary schools and from third grade higher middle education ENLACE PLANEA exams The instruments contain anchor questions measuring similar constructs at two moments in time for the same students The comparison of students s learning achievement in these two evaluation moments allows us to identify that there was progress in their performance after having completed their higher middle education regardless of the institution some individual characteristics and the place where the school is located Bivariate and multivariate analyzes are presented identifying differences in the students learning gains according to the subsystem of higher middle education in which they studied the school shift attended the type of secondary school from which they came from and their sex among other variable

Validation of the Satisfaction Scale of Basic Psychological Needs in Physical Education with the Incorporation of the Novelty in the Spanish Context

The purpose of the study was to validate to the physical education context, the Spanish version of the Scale of the Satisfaction of Psychological Needs toward the Physical Education classes of Menéndez and Fernández-Rio, with the incorporation of the novelty, since they contemplated its inclusion. In this study, 1444 students participated (mean = 15.34, standard deviation = 1.12) from several schools in Almeria. To analyze the psychometric properties of the scale, several analyses were carried out. The results offered support for both the four-factor structure and the higher-order model called satisfaction. The analysis of invariance with respect to gender showed that the factor structure of the questionnaire was invariant. The Cronbach alpha values were higher than 0.70 in the subscales. The results of this study demonstrated the reliability and validity of the Scale of the Satisfaction of Psychological Needs, with the incorporation of novelty in the Spanish context of Physical Education

T-Norm y desajuste léxico y acústico en reconocimiento de locutor dependiente de texto

Actas de las V Jornadas en Tecnología del Habla (JTH 2008)Este trabajo presenta un estudio extenso sobre T-norm aplicado a Reconocimiento de Locutor Dependiente de Texto, analizando también los problemas del desajuste léxico y acústico. Veremos cómo varían los resultados teniendo en cuenta la dependencia de género y realizando T-norm a nivel de frase, fonema y estado con cohortes de impostores de distintos tamaños. El estudio demuestra que implementar T-norm por fonema o estado puede llegar a conseguir mejoras relativas de hasta un 16% y que realizar una selección de cohorte basada en el género puede mejorar más aún los resultados con respecto al caso independiente de género

Physical Education Classes as a Precursor to the Mediterranean Diet and the Practice of Physical Activity

Physical activity and a healthy, balanced diet are remaining unresolved issues among young people. According to the World Health Organization, young people do not get enough exercise during the week, and physical education classes are the best way to promote healthy habits. This study aims to analyze how the role of the teacher influences the frustration of psychological needs, coping strategies, motivation, and the adoption of healthy eating habits through the Mediterranean diet and the regular practice of physical activity. The study involved 1031 boys and 910 girls between the ages of 13 and 18. To explain the relationships between the different variables included in this study, a model of structural equations has been developed. The results showed that autonomy support negatively predicted the frustration of four psychological needs. The failure to meet four psychological needs negatively predicted resilience. Likewise, resilience positively predicted autonomous motivation, and this positively predicted the Mediterranean diet and the practice of physical activity. Thus, the results obtained in the present study are in line with those of various studies wherein physical education classes were seen to help consolidate healthy living habits

Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/982531This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project