Pub Date : 2007-12-01DOI: 10.1109/ASRU.2007.4430112
Matthias H. Heie, E. Whittaker, Josef R. Novak, S. Furui
This paper presents a language modeling approach to sentence retrieval for Question Answering (QA) that we used in Question Answering on speech transcripts (QAst), a pilot task at the Cross Language Evaluation Forum (CLEF) evaluations 2007. A language model (LM) is generated for each sentence and these models are combined with document LMs to take advantage of contextual information. A query expansion technique using class models is proposed and included in our framework. Finally, our method's impact on exact answer extraction is evaluated. We show that combining sentence LMs with document LMs significantly improves sentence retrieval performance, and that this sentence retrieval approach leads to better answer extraction performance.
{"title":"A language modeling approach to question answering on speech transcripts","authors":"Matthias H. Heie, E. Whittaker, Josef R. Novak, S. Furui","doi":"10.1109/ASRU.2007.4430112","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430112","url":null,"abstract":"This paper presents a language modeling approach to sentence retrieval for Question Answering (QA) that we used in Question Answering on speech transcripts (QAst), a pilot task at the Cross Language Evaluation Forum (CLEF) evaluations 2007. A language model (LM) is generated for each sentence and these models are combined with document LMs to take advantage of contextual information. A query expansion technique using class models is proposed and included in our framework. Finally, our method's impact on exact answer extraction is evaluated. We show that combining sentence LMs with document LMs significantly improves sentence retrieval performance, and that this sentence retrieval approach leads to better answer extraction performance.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121491428","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430110
Keelan Evanini, David Suendermann-Oeft, R. Pieraccini
This paper compares six algorithms for call classification in the framework of a dialog system for automated troubleshooting. The comparison is carried out on large datasets, each consisting of over 100,000 utterances from two domains: television (TV) and Internet (INT). In spite of the high number of classes (79 for TV and 58 for INT), the best classifier (maximum entropy on word bigrams) achieved more than 77% classification accuracy on the TV dataset and 81% on the INT dataset.
{"title":"Call classification for automated troubleshooting on large corpora","authors":"Keelan Evanini, David Suendermann-Oeft, R. Pieraccini","doi":"10.1109/ASRU.2007.4430110","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430110","url":null,"abstract":"This paper compares six algorithms for call classification in the framework of a dialog system for automated troubleshooting. The comparison is carried out on large datasets, each consisting of over 100,000 utterances from two domains: television (TV) and Internet (INT). In spite of the high number of classes (79 for TV and 58 for INT), the best classifier (maximum entropy on word bigrams) achieved more than 77% classification accuracy on the TV dataset and 81% on the INT dataset.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121623531","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430132
J. Hershey, P. Olsen, Steven J. Rennie
Divergence measures are widely used tools in statistics and pattern recognition. The Kullback-Leibler (KL) divergence between two hidden Markov models (HMMs) would be particularly useful in the fields of speech and image recognition. Whereas the KL divergence is tractable for many distributions, including Gaussians, it is not in general tractable for mixture models or HMMs. Recently, variational approximations have been introduced to efficiently compute the KL divergence and Bhattacharyya divergence between two mixture models, by reducing them to the divergences between the mixture components. Here we generalize these techniques to approach the divergence between HMMs using a recursive backward algorithm. Two such methods are introduced, one of which yields an upper bound on the KL divergence, the other of which yields a recursive closed-form solution. The KL and Bhattacharyya divergences, as well as a weighted edit-distance technique, are evaluated for the task of predicting the confusability of pairs of words.
{"title":"Variational Kullback-Leibler divergence for Hidden Markov models","authors":"J. Hershey, P. Olsen, Steven J. Rennie","doi":"10.1109/ASRU.2007.4430132","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430132","url":null,"abstract":"Divergence measures are widely used tools in statistics and pattern recognition. The Kullback-Leibler (KL) divergence between two hidden Markov models (HMMs) would be particularly useful in the fields of speech and image recognition. Whereas the KL divergence is tractable for many distributions, including Gaussians, it is not in general tractable for mixture models or HMMs. Recently, variational approximations have been introduced to efficiently compute the KL divergence and Bhattacharyya divergence between two mixture models, by reducing them to the divergences between the mixture components. Here we generalize these techniques to approach the divergence between HMMs using a recursive backward algorithm. Two such methods are introduced, one of which yields an upper bound on the KL divergence, the other of which yields a recursive closed-form solution. The KL and Bhattacharyya divergences, as well as a weighted edit-distance technique, are evaluated for the task of predicting the confusability of pairs of words.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124385471","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430143
G. Saon, M. Picheny
We describe a cardinal-synchronous Viterbi decoder for statistical phrase-based machine translation which can operate on general ASR lattices (as opposed to confusion networks). The decoder implements constrained source reordering on the input lattice and makes use of an outbound distortion model to score the possible reorderings. The phrase table, representing the decoding search space, is encoded as a weighted finite state acceptor which is determined and minimized. At a high level, the search proceeds by performing simultaneous transitions in two pairs of automata: (input lattice, phrase table FSM) and (phrase table FSM, target language model). An alternative decoding strategy that we explore is to break the search into two independent subproblems: first, we perform monotone lattice decoding and find the best foreign path through the ASR lattice and then, we decode this path with reordering using standard sentence-based SMT. We report experimental results on several testsets of a large scale Arabic-to-English speech translation task in the context of the global autonomous language exploitation (or GALE) DARPA project. The results indicate that, for monotone search, lattice-based decoding outperforms 1-best decoding whereas for search with reordering, only the second decoding strategy was found to be superior to 1-best decoding. In both cases, the improvements hold only for shallow lattices.
{"title":"Lattice-based Viterbi decoding techniques for speech translation","authors":"G. Saon, M. Picheny","doi":"10.1109/ASRU.2007.4430143","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430143","url":null,"abstract":"We describe a cardinal-synchronous Viterbi decoder for statistical phrase-based machine translation which can operate on general ASR lattices (as opposed to confusion networks). The decoder implements constrained source reordering on the input lattice and makes use of an outbound distortion model to score the possible reorderings. The phrase table, representing the decoding search space, is encoded as a weighted finite state acceptor which is determined and minimized. At a high level, the search proceeds by performing simultaneous transitions in two pairs of automata: (input lattice, phrase table FSM) and (phrase table FSM, target language model). An alternative decoding strategy that we explore is to break the search into two independent subproblems: first, we perform monotone lattice decoding and find the best foreign path through the ASR lattice and then, we decode this path with reordering using standard sentence-based SMT. We report experimental results on several testsets of a large scale Arabic-to-English speech translation task in the context of the global autonomous language exploitation (or GALE) DARPA project. The results indicate that, for monotone search, lattice-based decoding outperforms 1-best decoding whereas for search with reordering, only the second decoding strategy was found to be superior to 1-best decoding. In both cases, the improvements hold only for shallow lattices.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133681294","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430137
A. Ljolje, Vincent Goffin
A barge-in system designed to reflect the design of the acoustic model used in commercial applications has been built and evaluated. It uses standard hidden Markov model structures, cepstral features and multiple hidden Markov models for both the speech and non-speech parts of the model. It is tested on a large number of real-world databases using noisy speech onset positions which were determined by forced alignment of lexical transcriptions with the recognition model. The ML trained model achieves low false rejection rates at the expense of high false acceptance rates. The discriminative training using the modified algorithm based on the maximum mutual information criterion reduces the false acceptance rates by a half, while preserving the low false rejection rates. Combining an energy based voice activity detector with the hidden Markov model based barge-in models achieves the best performance.
{"title":"Discriminative training of multi-state barge-in models","authors":"A. Ljolje, Vincent Goffin","doi":"10.1109/ASRU.2007.4430137","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430137","url":null,"abstract":"A barge-in system designed to reflect the design of the acoustic model used in commercial applications has been built and evaluated. It uses standard hidden Markov model structures, cepstral features and multiple hidden Markov models for both the speech and non-speech parts of the model. It is tested on a large number of real-world databases using noisy speech onset positions which were determined by forced alignment of lexical transcriptions with the recognition model. The ML trained model achieves low false rejection rates at the expense of high false acceptance rates. The discriminative training using the modified algorithm based on the maximum mutual information criterion reduces the false acceptance rates by a half, while preserving the low false rejection rates. Combining an energy based voice activity detector with the hidden Markov model based barge-in models achieves the best performance.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133540383","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430180
Björn Schuller, Bogdan Vlasenko, Ricardo Minguez, G. Rigoll, A. Wendemuth
In the search for a standard unit for use in recognition of emotion in speech, a whole turn, that is the full section of speech by one person in a conversation, is common. Within applications such turns often seem favorable. Yet, high effectiveness of sub-turn entities is known. In this respect a two-stage approach is investigated to provide higher temporal resolution by chunking of speech-turns according to acoustic properties, and multi-instance learning for turn-mapping after individual chunk analysis. For chunking fast pre-segmentation into emotionally quasi-stationary segments by one-pass Viterbi beam search with token passing basing on MFCC is used. Chunk analysis is realized by brute-force large feature space construction with subsequent subset selection, SVM classification, and speaker normalization. Extensive tests reveal differences compared to one-stage processing. Alternatively, syllables are used for chunking.
{"title":"Comparing one and two-stage acoustic modeling in the recognition of emotion in speech","authors":"Björn Schuller, Bogdan Vlasenko, Ricardo Minguez, G. Rigoll, A. Wendemuth","doi":"10.1109/ASRU.2007.4430180","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430180","url":null,"abstract":"In the search for a standard unit for use in recognition of emotion in speech, a whole turn, that is the full section of speech by one person in a conversation, is common. Within applications such turns often seem favorable. Yet, high effectiveness of sub-turn entities is known. In this respect a two-stage approach is investigated to provide higher temporal resolution by chunking of speech-turns according to acoustic properties, and multi-instance learning for turn-mapping after individual chunk analysis. For chunking fast pre-segmentation into emotionally quasi-stationary segments by one-pass Viterbi beam search with token passing basing on MFCC is used. Chunk analysis is realized by brute-force large feature space construction with subsequent subset selection, SVM classification, and speaker normalization. Extensive tests reveal differences compared to one-stage processing. Alternatively, syllables are used for chunking.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133101078","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430154
David Rybach, Stefan Hahn, C. Gollan, R. Schlüter, H. Ney
This paper describes the RWTH speech recognition system for Arabic. Several design aspects of the system, including cross-adaptation, multiple system design and combination, are analyzed. We summarize the semi-automatic lexicon generation for Arabic using a statistical approach to grapheme-to-phoneme conversion and pronunciation statistics. Furthermore, a novel ASR-based audio segmentation algorithm is presented. Finally, we discuss practical approaches for parallelized acoustic training and memory efficient lattice rescoring. Systematic results are reported on recent GALE evaluation corpora.
{"title":"Advances in Arabic broadcast news transcription at RWTH","authors":"David Rybach, Stefan Hahn, C. Gollan, R. Schlüter, H. Ney","doi":"10.1109/ASRU.2007.4430154","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430154","url":null,"abstract":"This paper describes the RWTH speech recognition system for Arabic. Several design aspects of the system, including cross-adaptation, multiple system design and combination, are analyzed. We summarize the semi-automatic lexicon generation for Arabic using a statistical approach to grapheme-to-phoneme conversion and pronunciation statistics. Furthermore, a novel ASR-based audio segmentation algorithm is presented. Finally, we discuss practical approaches for parallelized acoustic training and memory efficient lattice rescoring. Systematic results are reported on recent GALE evaluation corpora.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115684897","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430135
K. Schutte, James R. Glass
A method for acoustic modeling of speech is presented which is based on learning and detecting the occurrence of localized time-frequency patterns in a spectrogram. A boosting algorithm is applied to both build classifiers and perform feature selection from a large set of features derived by filtering spectrograms. Initial experiments are performed to discriminate digits in the Aurora database. The system succeeds in learning sequences of localized time-frequency patterns which are highly interpretable from an acoustic-phonetic viewpoint. While the work and the results are preliminary, they suggest that pursuing these techniques further could lead to new approaches to acoustic modeling for ASR which are more noise robust and offer better encoding of temporal dynamics than typical features such as frame-based cepstra.
{"title":"Speech recognition with localized time-frequency pattern detectors","authors":"K. Schutte, James R. Glass","doi":"10.1109/ASRU.2007.4430135","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430135","url":null,"abstract":"A method for acoustic modeling of speech is presented which is based on learning and detecting the occurrence of localized time-frequency patterns in a spectrogram. A boosting algorithm is applied to both build classifiers and perform feature selection from a large set of features derived by filtering spectrograms. Initial experiments are performed to discriminate digits in the Aurora database. The system succeeds in learning sequences of localized time-frequency patterns which are highly interpretable from an acoustic-phonetic viewpoint. While the work and the results are preliminary, they suggest that pursuing these techniques further could lead to new approaches to acoustic modeling for ASR which are more noise robust and offer better encoding of temporal dynamics than typical features such as frame-based cepstra.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114384991","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430149
F. Stouten, J. Martens
The development of an automatic speech recognizer (ASR) that can accurately recognize spoken names belonging to a large lexicon, is still a big challenge. One of the bottlenecks is that many names contain elements of a foreign language origin, and native speakers can adopt very different pronunciations of these elements, ranging from completely nativized to completely foreignized pronunciations. In this paper we further develop a recently proposed method for improving the recognition of foreign proper names spoken by native speakers. The main idea is to combine the standard acoustic model scores with scores emerging from a phonologically inspired back-off model that was trained on native speech only. This means that the proposed method does not require the development of any foreign phoneme models on foreign speech data. By applying our method on a baseline Dutch recognizer (comprising Dutch acoustic models) we could reduce the name error rate for French and English names by a considerable amount.
{"title":"Dealing with cross-lingual aspects in spoken name recognition","authors":"F. Stouten, J. Martens","doi":"10.1109/ASRU.2007.4430149","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430149","url":null,"abstract":"The development of an automatic speech recognizer (ASR) that can accurately recognize spoken names belonging to a large lexicon, is still a big challenge. One of the bottlenecks is that many names contain elements of a foreign language origin, and native speakers can adopt very different pronunciations of these elements, ranging from completely nativized to completely foreignized pronunciations. In this paper we further develop a recently proposed method for improving the recognition of foreign proper names spoken by native speakers. The main idea is to combine the standard acoustic model scores with scores emerging from a phonologically inspired back-off model that was trained on native speech only. This means that the proposed method does not require the development of any foreign phoneme models on foreign speech data. By applying our method on a baseline Dutch recognizer (comprising Dutch acoustic models) we could reduce the name error rate for French and English names by a considerable amount.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123965974","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 : 2007-12-01DOI: 10.1109/ASRU.2007.4430158
B. Ramabhadran, O. Siohan, A. Sethy
TC-STAR is an European Union funded speech to speech translation project to transcribe, translate and synthesize European Parliamentary Plenary Speeches (EPPS). This paper describes IBM's English speech recognition system submitted to the TC-STAR 2007 Evaluation. Language model adaptation based on clustering and data selection using relative entropy minimization provided significant gains in the 2007 evaluation. The additional advances over the 2006 system that we present in this paper include unsupervised training of acoustic and language models; a system architecture that is based on cross-adaptation across complementary systems and system combination through generation of an ensemble of systems using randomized decision tree state-tying. These advances reduced the error rate by 30% relative over the best-performing system in the TC-STAR 2006 evaluation on the 2006 English development and evaluation test sets, and produced one of the best performing systems on the 2007 evaluation in English with a word error rate of 7.1%.
{"title":"The IBM 2007 speech transcription system for European parliamentary speeches","authors":"B. Ramabhadran, O. Siohan, A. Sethy","doi":"10.1109/ASRU.2007.4430158","DOIUrl":"https://doi.org/10.1109/ASRU.2007.4430158","url":null,"abstract":"TC-STAR is an European Union funded speech to speech translation project to transcribe, translate and synthesize European Parliamentary Plenary Speeches (EPPS). This paper describes IBM's English speech recognition system submitted to the TC-STAR 2007 Evaluation. Language model adaptation based on clustering and data selection using relative entropy minimization provided significant gains in the 2007 evaluation. The additional advances over the 2006 system that we present in this paper include unsupervised training of acoustic and language models; a system architecture that is based on cross-adaptation across complementary systems and system combination through generation of an ensemble of systems using randomized decision tree state-tying. These advances reduced the error rate by 30% relative over the best-performing system in the TC-STAR 2006 evaluation on the 2006 English development and evaluation test sets, and produced one of the best performing systems on the 2007 evaluation in English with a word error rate of 7.1%.","PeriodicalId":371729,"journal":{"name":"2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122719420","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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