Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition

We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representation of the test posteriors using this dictionary enables projection to the space of training data. Relying on the fact that the intrinsic dimensions of the posterior subspaces are indeed very small and the matrix of all posteriors belonging to a class has a very low rank, we demonstrate how low-dimensional structures enable further enhancement of the posteriors and rectify the spurious errors due to mismatch conditions. The enhanced acoustic modeling method leads to improvements in continuous speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in both clean and noisy conditions, where upto 15.4% relative reduction in word error rate (WER) is achieved

Low-Rank Representation For Enhanced Deep Neural Network Acoustic Models

Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DNN) to estimate class-conditional posterior probabilities. The posterior probabilities are used for acoustic modeling in hidden Markov models (HMM), and form a hybrid DNN-HMM which is now the leading edge approach to solve ASR problems. The present work builds upon the hypothesis that the optimal acoustic models are sparse and lie on multiple low-rank probability subspaces. Hence, the main goal of this Master project aimed at investigating different ways to restructure the DNN outputs using low-rank representation. Exploiting a large number of training posterior vectors, the underlying low-dimensional subspace can be identified, and low-rank decomposition enables separation of the “optimal” posteriors from the spurious (unstructured) uncertainties at the DNN output. Experiments demonstrate that low-rank representation can enhance posterior probability estimation, and lead to higher ASR accuracy. The posteriors are grouped according to their subspace similarities, and structured through low-rank decomposition. Furthermore, a novel hashing technique is proposed exploiting the low-rank property of posterior subspaces that enables fast search in the space of posterior exemplars

Phonetic and Phonological Posterior Search Space Hashing Exploiting Class-Specific Sparsity Structures

This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small number of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of neighboring exemplars. The $k$ nearest neighbor ($k$NN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phonetic classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of $k$NN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

Efficient Posterior Exemplar Search Space Hashing Exploiting Class-Specific Sparsity Structures

This paper shows that exemplar-based speech processing using class-conditional posterior probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity structures. The posterior probabilities are estimated for phonetic and phonological classes using deep neural network (DNN) computational framework. Exploiting the class-specific sparsity leads to a simple quantized posterior hashing procedure to reduce the search space of posterior exemplars. To that end, small subset of quantized posteriors are regarded as representatives of the posterior space and used as hash keys to index subsets of similar exemplars. The $k$ nearest neighbor ($k$NN) method is applied for posterior based classification problems. The phonetic posterior probabilities are used as exemplars for phoneme classification whereas the phonological posteriors are used as exemplars for automatic prosodic event detection. Experimental results demonstrate that posterior hashing improves the efficiency of $k$NN classification drastically. This work encourages the use of posteriors as discriminative exemplars appropriate for large scale speech classification tasks

Assessing the effectiveness of Multi-Sector Partnerships to manage droughts: The case of the Jucar river basin

[EN] South-east Spain is a drought prone area, characterized by climate variability and water scarcity. The Jucar River Basin, located in Eastern Spain, has suffered many historical droughts with significant socio-economic impacts. For nearly a hundred years, the institutional and non-institutional strategies to cope with droughts have been successful through the development of institutions and partnerships for drought management including multiple actors. In this paper, we show how the creation and institutionalisation of Multi-Sector Partnerships (MSPs) has supported the development of an efficient drought management. Furthermore, we analyze the performance of one of the suggested instruments by the partnership related to drought management in the basin. Two methodologies are used for these purposes. On one hand, the Capital Approach Framework to analyze the effectiveness of the governance processes in a particular partnership (Permanent Drought Commission), which aims to highlight the governance strength and weakness of the MSP for enhancing drought management in the Jucar River Basin. Through a dynamic analysis of the changes that the partnership has undergone over time to successfully deal with droughts, its effectiveness on drought management is demonstrated. On the other hand, an econometric approach is used to analyze the economic efficiency of the emergency drought wells as one of the key drought mitigation measures suggested by the Permanent Drought Commission and implemented. The results demonstrate the potential and efficiency of applying drought wells as mitigation measures (significant reduction of economic losses, around 50 M(sic) during the drought period, 2005-2008).We acknowledge the project ENHANCE (Grant Agreement number 308438) for the financial support to this research. As well as, we thank the Jucar River Basin stakeholders for providing the help to get data for the analysis. The data used are listed in the references, tables, and Appendix A.Carmona, M.; Mañez, M.; Andreu Álvarez, J.; Pulido-Velazquez, M.; Haro Monteagudo, D.; Lopez-Nicolas, A.; Cremades, R. (2017). Assessing the effectiveness of Multi-Sector Partnerships to manage droughts: The case of the Jucar river basin. Earth's Future. 5(7):750-770. https://doi.org/10.1002/2017EF000545S7507705

La Lanterne : journal politique quotidien

06 juin 19021902/06/06 (N9174,A25)