System-independent ASR error detection and classification using Recurrent Neural Network

This paper addresses errors in continuous Automatic Speech Recognition (ASR) in two stages: error detection and error type classification. Unlike the majority of research in this field, we propose to handle the recognition errors independently from the ASR decoder. We first establish an effective set of generic features derived exclusively from the recognizer output to compensate for the absence of ASR decoder information. Then, we apply a variant Recurrent Neural Network (V-RNN) based models for error detection and error type classification. Such model learn additional information to the recognized word classification using label dependency. As a result, experiments on Multi-Genre Broadcast Media corpus have shown that the proposed generic features setup leads to achieve competitive performances, compared to state of the art systems in both tasks. Furthermore, we have shown that V-RNN trained on the proposed feature set appear to be an effective classifier for the ASR error detection with an Accuracy of 85.43%

On the voice-activated question answering

[EN] Question answering (QA) is probably one of the most challenging tasks in the field of natural language processing. It requires search engines that are capable of extracting concise, precise fragments of text that contain an answer to a question posed by the user. The incorporation of voice interfaces to the QA systems adds a more natural and very appealing perspective for these systems. This paper provides a comprehensive description of current state-of-the-art voice-activated QA systems. Finally, the scenarios that will emerge from the introduction of speech recognition in QA will be discussed. © 2006 IEEE.This work was supported in part by Research Projects TIN2009-13391-C04-03 and TIN2008-06856-C05-02. This paper was recommended by Associate Editor V. Marik.Rosso, P.; Hurtado Oliver, LF.; Segarra Soriano, E.; Sanchís Arnal, E. (2012). On the voice-activated question answering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 42(1):75-85. https://doi.org/10.1109/TSMCC.2010.2089620S758542

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin