Data Balancing for Efficient Training of Hybrid ANN/HMM Automatic Speech Recognition Systems

Hybrid speech recognizers, where the estimation of the emission pdf of the states of Hidden Markov Models (HMMs), usually carried out using Gaussian Mixture Models (GMMs), is substituted by Artificial Neural Networks (ANNs) have several advantages over the classical systems. However, to obtain performance improvements, the computational requirements are heavily increased because of the need to train the ANN. Departing from the observation of the remarkable skewness of speech data, this paper proposes sifting out the training set and balancing the amount of samples per class. With this method the training time has been reduced 18 times while obtaining performances similar to or even better than those with the whole database, especially in noisy environments. However, the application of these reduced sets is not straightforward. To avoid the mismatch between training and testing conditions created by the modification of the distribution of the training data, a proper scaling of the a posteriori probabilities obtained and a resizing of the context window need to be performed as demonstrated in the paper.This work was supported in part by the regional grant (Comunidad Autónoma de Madrid-UC3M) CCG06-UC3M/TIC-0812 and in part by a project funded by the Spanish Ministry of Science and Innovation (TEC 2008-06382).Publicad

Real-time robust automatic speech recognition using compact support vector machines

In the last years, support vector machines (SVMs) have shown excellent performance in many applications, especially in the presence of noise. In particular, SVMs offer several advantages over artificial neural networks (ANNs) that have attracted the attention of the speech processing community. Nevertheless, their high computational requirements prevent them from being used in practice in automatic speech recognition (ASR), where ANNs have proven to be successful. The high complexity of SVMs in this context arises from the use of huge speech training databases with millions of samples and highly overlapped classes. This paper suggests the use of a weighted least squares (WLS) training procedure that facilitates the possibility of imposing a compact semiparametric model on the SVM, which results in a dramatic complexity reduction. Such a complexity reduction with respect to conventional SVMs, which is between two and three orders of magnitude, allows the proposed hybrid WLS-SVC/HMM system to perform real-time speech decoding on a connected-digit recognition task (SpeechDat Spanish database). The experimental evaluation of the proposed system shows encouraging performance levels in clean and noisy conditions, although further improvements are required to reach the maturity level of current context-dependent HMM based recognizers.Spanish Ministry of Science and Innovation TEC 2008-06382 and TEC 2008-02473 and Comunidad Autónoma de Madrid-UC3M CCG10-UC3M/TIC-5304.Publicad

Promoting social capital, self-management and health literacy in older adults through a group-based intervention delivered in low-income urban areas : results of the randomized trial AEQUALIS

Altres ajuts: Recercaixa (2014ACUP00207)Background: Evidence is scarce on how to promote health and decrease cumulative inequalities for disadvantaged older people. Downstream complex interventions focusing on intermediate factors (self-management, health literacy and social capital) may have the potential to mitigate the inequitable impacts of social determinants in health. The aim of the AEQUALIS study was to assess the effectiveness of a group-based intervention to improve self-perceived health as indicator of health inequality. Methods: Pragmatic randomised clinical trial addressed to older adults (≥ 60 years) living in urban disadvantaged areas with low self-perceived health. The intervention was delivered in primary care settings and community assets between 2015 and 2017 and consisted in 12 weekly sessions. The primary outcome was self-perceived health assessed in two ways: with the first item of the SF-12 questionnaire, and with the EQ-5D visual analog scale. Secondary outcomes were health-related quality of life, social capital, self-management, mental health and use of health services. Outcomes were assessed at baseline, post intervention and follow-up at 9 months after the end of the intervention. Results: 390 people were allocated to the intervention group (IG) or the control group (CG) and 194 participants and 164 were included in the data analysis, respectively. Self perceived health as primary outcome assessed with SF-12-1 was not specifically affected by the intervention, but with the EQ-5D visual analog scale showed a significant increase at one-year follow-up only in the IG (MD=4.80, 95%CI [1.09, 8.52]). IG group improved health literacy in terms of a better understanding of medical information (− 0.62 [− 1.10, − 0.13]). The mental component of SF-12 improved (3.77 [1.82, 5.73]), and depressive symptoms decreased at post-intervention (− 1.26 [− 1.90, − 0.63]), and at follow-up (− 0.95 [− 1.62, − 0.27]). The use of antidepressants increased in CG at the follow-up (1.59 [0.33, 2.86]), while it remained stable in the IG. Conclusions: This study indicates that a group intervention with a strong social component, conducted in primary health care and community assets, shows promising effects on mental health and can be used as a strategy for health promotion among older adults in urban disadvantaged areas. Trial registration: ClinicalTrials.gov, NCT02733523. Registered 11 April 2016 - Retrospectively registere

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Hybrid models for automatic speech recognition: a comparison of classical ANN and kernel based methods

Support Vector Machines (SVM) are state-of-the-art methods for machine learning but share with more classical Artificial Neural Networks (ANN) the difficulty of their application to temporally variable input patterns. This is the case in Automatic Speech Recognition (ASR). In this paper we have recalled the solutions provided in the past for ANN and applied them to SVMs performing a comparison between them. Preliminary results show a similar behaviour which results encouraging if we take into account the novelty of the SVM systems in comparison with classical ANNs. The envisioned ways of improvement are outlined in the paper. 1