Protein alignment based on higher order conditional random fields for template-based modeling

The query-template alignment of proteins is one of the most critical steps of template-based modeling methods used to predict the 3D structure of a query protein. This alignment can be interpreted as a temporal classification or structured prediction task and first order Conditional Random Fields have been proposed for protein alignment and proven to be rather successful. Some other popular structured prediction problems, such as speech or image classification, have gained from the use of higher order Conditional Random Fields due to the well known higher order correlations that exist between their labels and features. In this paper, we propose and describe the use of higher order Conditional Random Fields for query-template protein alignment. The experiments carried out on different public datasets validate our proposal, especially on distantly-related protein pairs which are the most difficult to align.This research was supported by Project P12.TIC.1485 funded by Consejeria de Economia, Innovacion y Ciencia (Junta de Andalucia) and Spanish MINECO/FEDER Project TEC2016-80141- P

Protein Fold Recognition from Sequences using Convolutional and Recurrent Neural Networks

The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the protein fold recognition task. Our neural network model combines 1D-convolutional layers with gated recurrent unit (GRU) layers. The GRU cells, as recurrent layers, cope with the processing issues associated to the highly variable protein sequence lengths and so extract a fold-related embedding of fixed size for each protein domain. These embeddings are then used to perform the pairwise fold recognition task, which is based on transferring the fold type of the most similar template structure. We compare our model with several template-based and deep learning-based methods from the state-of-the-art. The evaluation results over the well-known LINDAHL and SCOP_TEST sets,along with a proposed LINDAHL test set updated to SCOP 1.75, show that our embeddings perform significantly better than these methods, specially at the fold level. Supplementary material, source code and trained models are available at http://sigmat.ugr.es/~amelia/CNN-GRU-RF+/

Robust ASR using neural network based speech enhancement and feature simulation

Submitted to ICASSP 2020International audienceWe consider the problem of robust automatic speech recognition (ASR) in the context of the CHiME-3 Challenge. The proposed system combines three contributions. First, we propose a deep neural network (DNN) based multichannel speech enhancement technique, where the speech and noise spectra are estimated using a DNN based regressor and the spatial parameters are derived in an expectation-maximization (EM) like fashion. Second, a conditional restricted Boltz-mann machine (CRBM) model is trained using the obtained enhanced speech and used to generate simulated training and development datasets. The goal is to increase the similarity between simulated and real data, so as to increase the benefit of multicondition training. Finally, we make some changes to the ASR backend. Our system ranked 4th among 25 entrie

Técnicas de reconocimiento robusto de la voz basadas en el pitch

Tesis Univ. Granada. Departamento de Teoría de la Señal Telemática y Comunicaciones. Leída el 5 de septiembre de 201

Pronunciation variation in read and conversational austrian german

International audienceThis paper presents the first large-scale analysis of pronunciation variation in conversational Austrian German. Whereas for the varieties of German spoken in Germany, conversational speech has been given noticeable attention in the fields of linguistics and automatic speech recognition, for conversational Austrian there is a lack in speech resources and tools as well as linguistic and phonetic studies. Based on the recently collected GRASS corpus, we provide (methods for the creation of) a pronunciation dictionary and (tools for the creation of) broad phonetic transcriptions for Austrian German. Subsequently, we present a comparative analysis of the occurrence of phonological and reduction rules in read and conversational speech. We find that whereas some rules are specific for the Austrian Standard variant and thus occur in both speech styles (e.g., the realization of /z/ as [s]), other rules are specific for conversational speech (e.g., the realization of /a:/ as [o:]. Overall, our results show that less words are produced with the citation form for conversational Austrian German (37.8% ) than for other languages of the same style (e.g., Dutch conversations: 56%)

What we can learn from asr errors about low-resourced languages: a case-study of luxembourgish and austrian

International audienc

What we can learn from asr errors about low-resourced languages: a case-study of luxembourgish and austrian

International audienc