Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424161
Filip Jurcícek
Reinforcement learning methods have been successfully used to optimise dialogue strategies in statistical dialogue systems. Typically, reinforcement techniques learn on-policy i.e., the dialogue strategy is updated online while the system is interacting with a user. An alternative to this approach is off-policy reinforcement learning, which estimates an optimal dialogue strategy offline from a fixed corpus of previously collected dialogues. This paper proposes a novel off-policy reinforcement learning method based on natural policy gradients and importance sampling. The algorithm is evaluated on a spoken dialogue system in the tourist information domain. The experiments indicate that the proposed method learns a dialogue strategy, which significantly outperforms the baseline handcrafted dialogue policy.
{"title":"Reinforcement learning for spoken dialogue systems using off-policy natural gradient method","authors":"Filip Jurcícek","doi":"10.1109/SLT.2012.6424161","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424161","url":null,"abstract":"Reinforcement learning methods have been successfully used to optimise dialogue strategies in statistical dialogue systems. Typically, reinforcement techniques learn on-policy i.e., the dialogue strategy is updated online while the system is interacting with a user. An alternative to this approach is off-policy reinforcement learning, which estimates an optimal dialogue strategy offline from a fixed corpus of previously collected dialogues. This paper proposes a novel off-policy reinforcement learning method based on natural policy gradients and importance sampling. The algorithm is evaluated on a spoken dialogue system in the tourist information domain. The experiments indicate that the proposed method learns a dialogue strategy, which significantly outperforms the baseline handcrafted dialogue policy.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424199
Gina-Anne Levow, Siwei Wang
Verbal feedback provides important cues in establishing interactional rapport. The challenge of recognizing contexts for verbal feedback largely arises from relative sparseness and optionality. In addition, cross-language and inter-speaker variations can make recognition more difficult. In this paper, we show that boosting can improve accuracy in recognizing contexts for verbal feedback based on prosodic cues. In our experiments, we use dyads from three languages (English, Spanish and Arabic) to evaluate two boosting methods, generalized Adaboost and Gradient Boosting Trees, against Support Vector Machines (SVMs) and a naive baseline, with explicit oversampling on the minority verbal feedback instances. We find that both boosting methods outperform the baseline and SVM classifiers. Analysis of the feature weighting by the boosted classifiers highlights differences and similarities in the prosodic cues employed by members of these diverse language/cultural groups.
{"title":"Employing boosting to compare cues to verbal feedback in multi-lingual dialog","authors":"Gina-Anne Levow, Siwei Wang","doi":"10.1109/SLT.2012.6424199","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424199","url":null,"abstract":"Verbal feedback provides important cues in establishing interactional rapport. The challenge of recognizing contexts for verbal feedback largely arises from relative sparseness and optionality. In addition, cross-language and inter-speaker variations can make recognition more difficult. In this paper, we show that boosting can improve accuracy in recognizing contexts for verbal feedback based on prosodic cues. In our experiments, we use dyads from three languages (English, Spanish and Arabic) to evaluate two boosting methods, generalized Adaboost and Gradient Boosting Trees, against Support Vector Machines (SVMs) and a naive baseline, with explicit oversampling on the minority verbal feedback instances. We find that both boosting methods outperform the baseline and SVM classifiers. Analysis of the feature weighting by the boosted classifiers highlights differences and similarities in the prosodic cues employed by members of these diverse language/cultural groups.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130559027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424201
K. Laskowski
Stochastic turn-taking models have traditionally been implemented as N-grams, which condition predictions on recent binary-valued speech/non-speech contours. The current work re-implements this function using feed-forward neural networks, capable of accepting binary- as well as continuous-valued features; performance is shown to asymptotically approach that of the N-gram baseline as model complexity increases. The conditioning context is then extended to leverage loudness contours. Experiments indicate that the additional sensitivity to loudness considerably decreases average cross entropy rates on unseen data, by 0.03 bits per framing interval of 100 ms. This reduction is shown to make loudness-sensitive conversants capable of better predictions, with attention memory requirements at least 5 times smaller and responsiveness latency at least 10 times shorter than the loudness-insensitive baseline.
{"title":"Exploiting loudness dynamics in stochastic models of turn-taking","authors":"K. Laskowski","doi":"10.1109/SLT.2012.6424201","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424201","url":null,"abstract":"Stochastic turn-taking models have traditionally been implemented as N-grams, which condition predictions on recent binary-valued speech/non-speech contours. The current work re-implements this function using feed-forward neural networks, capable of accepting binary- as well as continuous-valued features; performance is shown to asymptotically approach that of the N-gram baseline as model complexity increases. The conditioning context is then extended to leverage loudness contours. Experiments indicate that the additional sensitivity to loudness considerably decreases average cross entropy rates on unseen data, by 0.03 bits per framing interval of 100 ms. This reduction is shown to make loudness-sensitive conversants capable of better predictions, with attention memory requirements at least 5 times smaller and responsiveness latency at least 10 times shorter than the loudness-insensitive baseline.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131381917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424247
Stefan Ziegler, Bogdan Ludusan, G. Gravier
In this work, we present a new approach for the classification and detection of speech units for the use in landmark or event-based speech recognition systems. We use segmentation to model any time-variable speech unit by a fixed-dimensional observation vector, in order to train a committee of boosted decision stumps on labeled training data. Given an unknown speech signal, the presence of a desired speech unit is estimated by searching for each time frame the corresponding segment, that provides the maximum classification score. This approach improves the accuracy of a phoneme classification task by 1.7%, compared to classification using HMMs. Applying this approach to the detection of broad phonetic landmarks inside a landmark-driven HMM-based speech recognizer significantly improves speech recognition.
{"title":"Towards a new speech event detection approach for landmark-based speech recognition","authors":"Stefan Ziegler, Bogdan Ludusan, G. Gravier","doi":"10.1109/SLT.2012.6424247","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424247","url":null,"abstract":"In this work, we present a new approach for the classification and detection of speech units for the use in landmark or event-based speech recognition systems. We use segmentation to model any time-variable speech unit by a fixed-dimensional observation vector, in order to train a committee of boosted decision stumps on labeled training data. Given an unknown speech signal, the presence of a desired speech unit is estimated by searching for each time frame the corresponding segment, that provides the maximum classification score. This approach improves the accuracy of a phoneme classification task by 1.7%, compared to classification using HMMs. Applying this approach to the detection of broad phonetic landmarks inside a landmark-driven HMM-based speech recognizer significantly improves speech recognition.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123344485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424266
Ian Kaplan, Andrew Rosenberg
In this paper, we describe investigations into the speech used in American Presidential and Vice-Presidential debates. We explore possible transcript-based features that may correlate with personally appealing or politically persuasive language. We identify, with chi-squared analysis, features that correlate with success in the debates. We find that with a set of surface-level features from historical debates, we can predict the winners of presidential debates with success moderately above chance.
{"title":"Analysis of speech transcripts to predict winners of U.S. Presidential and Vice-Presidential debates","authors":"Ian Kaplan, Andrew Rosenberg","doi":"10.1109/SLT.2012.6424266","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424266","url":null,"abstract":"In this paper, we describe investigations into the speech used in American Presidential and Vice-Presidential debates. We explore possible transcript-based features that may correlate with personally appealing or politically persuasive language. We identify, with chi-squared analysis, features that correlate with success in the debates. We find that with a set of surface-level features from historical debates, we can predict the winners of presidential debates with success moderately above chance.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122571775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424218
Matthew Henderson, Milica Gasic, Blaise Thomson, P. Tsiakoulis, Kai Yu, S. Young
Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output.
{"title":"Discriminative spoken language understanding using word confusion networks","authors":"Matthew Henderson, Milica Gasic, Blaise Thomson, P. Tsiakoulis, Kai Yu, S. Young","doi":"10.1109/SLT.2012.6424218","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424218","url":null,"abstract":"Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following [1], the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130603733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424249
Daniel Bolaños
This article describes the design of Bavieca, an open-source speech recognition toolkit intended for speech research and system development. The toolkit supports lattice-based discriminative training, wide phonetic-context, efficient acoustic scoring, large n-gram language models, and the most common feature and model transformations. Bavieca is written entirely in C++ and presents a simple and modular design with an emphasis on scalability and reusability. Bavieca achieves competitive results in standard benchmarks. The toolkit is distributed under the highly unrestricted Apache 2.0 license, and is freely available on SourceForge.
{"title":"The Bavieca open-source speech recognition toolkit","authors":"Daniel Bolaños","doi":"10.1109/SLT.2012.6424249","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424249","url":null,"abstract":"This article describes the design of Bavieca, an open-source speech recognition toolkit intended for speech research and system development. The toolkit supports lattice-based discriminative training, wide phonetic-context, efficient acoustic scoring, large n-gram language models, and the most common feature and model transformations. Bavieca is written entirely in C++ and presents a simple and modular design with an emphasis on scalability and reusability. Bavieca achieves competitive results in standard benchmarks. The toolkit is distributed under the highly unrestricted Apache 2.0 license, and is freely available on SourceForge.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131501436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424267
Na Yang, R. Muraleedharan, J. Kohl, I. Demirkol, W. Heinzelman, Melissa L. Sturge‐Apple
Emotion classification is essential for understanding human interactions and hence is a vital component of behavioral studies. Although numerous algorithms have been developed, the emotion classification accuracy is still short of what is desired for the algorithms to be used in real systems. In this paper, we evaluate an approach where basic acoustic features are extracted from speech samples, and the One-Against-All (OAA) Support Vector Machine (SVM) learning algorithm is used. We use a novel hybrid kernel, where we choose the optimal kernel functions for the individual OAA classifiers. Outputs from the OAA classifiers are normalized and combined using a thresholding fusion mechanism to finally classify the emotion. Samples with low `relative confidence' are left as `unclassified' to further improve the classification accuracy. Results show that the decision-level recall of our approach for six-class emotion classification is 80.5%, outperforming a state-of-the-art approach that uses the same dataset.
{"title":"Speech-based emotion classification using multiclass SVM with hybrid kernel and thresholding fusion","authors":"Na Yang, R. Muraleedharan, J. Kohl, I. Demirkol, W. Heinzelman, Melissa L. Sturge‐Apple","doi":"10.1109/SLT.2012.6424267","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424267","url":null,"abstract":"Emotion classification is essential for understanding human interactions and hence is a vital component of behavioral studies. Although numerous algorithms have been developed, the emotion classification accuracy is still short of what is desired for the algorithms to be used in real systems. In this paper, we evaluate an approach where basic acoustic features are extracted from speech samples, and the One-Against-All (OAA) Support Vector Machine (SVM) learning algorithm is used. We use a novel hybrid kernel, where we choose the optimal kernel functions for the individual OAA classifiers. Outputs from the OAA classifiers are normalized and combined using a thresholding fusion mechanism to finally classify the emotion. Samples with low `relative confidence' are left as `unclassified' to further improve the classification accuracy. Results show that the decision-level recall of our approach for six-class emotion classification is 80.5%, outperforming a state-of-the-art approach that uses the same dataset.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115376629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424165
Milica Gasic, Matthew Henderson, Blaise Thomson, P. Tsiakoulis, S. Young
The partially observable Markov decision process (POMDP) has been proposed as a dialogue model that enables automatic improvement of the dialogue policy and robustness to speech understanding errors. It requires, however, a large number of dialogues to train the dialogue policy. Gaussian processes (GP) have recently been applied to POMDP dialogue management optimisation showing an ability to substantially increase the speed of learning. Here, we investigate this further using the Bayesian Update of Dialogue State dialogue manager. We show that it is possible to apply Gaussian processes directly to the belief state, removing the need for a parametric policy representation. In addition, the resulting policy learns significantly faster while maintaining operational performance.
{"title":"Policy optimisation of POMDP-based dialogue systems without state space compression","authors":"Milica Gasic, Matthew Henderson, Blaise Thomson, P. Tsiakoulis, S. Young","doi":"10.1109/SLT.2012.6424165","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424165","url":null,"abstract":"The partially observable Markov decision process (POMDP) has been proposed as a dialogue model that enables automatic improvement of the dialogue policy and robustness to speech understanding errors. It requires, however, a large number of dialogues to train the dialogue policy. Gaussian processes (GP) have recently been applied to POMDP dialogue management optimisation showing an ability to substantially increase the speed of learning. Here, we investigate this further using the Bayesian Update of Dialogue State dialogue manager. We show that it is possible to apply Gaussian processes directly to the belief state, removing the need for a parametric policy representation. In addition, the resulting policy learns significantly faster while maintaining operational performance.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116297449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-12-01DOI: 10.1109/SLT.2012.6424257
Ghada Alharbi, Thomas Hain
In a multimedia world it is now common to record professional presentations, on video or with audio only. Such recordings include talks and academic lectures, which are becoming a valuable resource for students and professionals alike. However, organising such material from a diverse set of disciplines seems to be not an easy task. One way to address this problem is to build an Automatic Speech Recognition (ASR) system in order to use its output for analysing such materials. In this work ASR results for lectures from diverse sources are presented. The work is based on a new collection of data, obtained by the Liberated Learning Consortium (LLC). The study's primary goals are two-fold: first to show variability across disciplines from an ASR perspective, and how to choose sources for the construction of language models (LMs); second, to provide an analysis of the lecture transcription for automatic determination of structures in lecture discourse. In particular, we investigate whether there are properties common to lectures from different disciplines. This study focuses on textual features. Lectures are multimodal experiences - it is not clear whether textual features alone are sufficient for the recognition of such common elements, or other features, e.g. acoustic features such as the speaking rate, are needed. The results show that such common properties are retained across disciplines even on ASR output with a Word Error Rate (WER) of 30%.
{"title":"Automatic transcription of academic lectures from diverse disciplines","authors":"Ghada Alharbi, Thomas Hain","doi":"10.1109/SLT.2012.6424257","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424257","url":null,"abstract":"In a multimedia world it is now common to record professional presentations, on video or with audio only. Such recordings include talks and academic lectures, which are becoming a valuable resource for students and professionals alike. However, organising such material from a diverse set of disciplines seems to be not an easy task. One way to address this problem is to build an Automatic Speech Recognition (ASR) system in order to use its output for analysing such materials. In this work ASR results for lectures from diverse sources are presented. The work is based on a new collection of data, obtained by the Liberated Learning Consortium (LLC). The study's primary goals are two-fold: first to show variability across disciplines from an ASR perspective, and how to choose sources for the construction of language models (LMs); second, to provide an analysis of the lecture transcription for automatic determination of structures in lecture discourse. In particular, we investigate whether there are properties common to lectures from different disciplines. This study focuses on textual features. Lectures are multimodal experiences - it is not clear whether textual features alone are sufficient for the recognition of such common elements, or other features, e.g. acoustic features such as the speaking rate, are needed. The results show that such common properties are retained across disciplines even on ASR output with a Word Error Rate (WER) of 30%.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126434240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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