Pub Date : 2014-12-04DOI: 10.1109/IALP.2014.6973518
H. Patil, S. Adarsa
Many investigations on speech nonlinearities have been carried out and these studies provide strong evidences to support nonlinear system modelling of speech production. The nonlinear characteristics that these studies point to are analogous to chaotic systems. This paper aims to provide evidence of chaotic nature of speech signal and use it for feature extraction to distinguish synthetic and natural speech. The feature used to extract chaos is Lyapunov Exponent (LE). The synthetic speech is found to have higher values of LE in comparison with natural speech. We propose a new feature based on LE for detection of synthetic speech. The synthetic speech used is from Hidden Markov Model (HMM)-based speech synthesis system (HTS) trained using low resource Indian language-Gujarati. This work may find its application for improving robustness of speaker verification (SV) systems against imposture attack using synthetic speech.
{"title":"Nonlinear analysis of natural vs. HTS-based synthetic speech","authors":"H. Patil, S. Adarsa","doi":"10.1109/IALP.2014.6973518","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973518","url":null,"abstract":"Many investigations on speech nonlinearities have been carried out and these studies provide strong evidences to support nonlinear system modelling of speech production. The nonlinear characteristics that these studies point to are analogous to chaotic systems. This paper aims to provide evidence of chaotic nature of speech signal and use it for feature extraction to distinguish synthetic and natural speech. The feature used to extract chaos is Lyapunov Exponent (LE). The synthetic speech is found to have higher values of LE in comparison with natural speech. We propose a new feature based on LE for detection of synthetic speech. The synthetic speech used is from Hidden Markov Model (HMM)-based speech synthesis system (HTS) trained using low resource Indian language-Gujarati. This work may find its application for improving robustness of speaker verification (SV) systems against imposture attack using synthetic speech.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117098474","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973508
Mohammadi Zaki, Nirmesh J. Shah, H. Patil
Phonetic segmentation plays a key role in developing various speech applications. In this work, we propose to use various features for automatic phonetic segmentation task for forced Viterbi alignment and compare their effectiveness. We propose to use novel multiscale fractal dimension-based features concatenated with Mel-Frequency Cepstral Coefficients (MFCC). The novel features are expected to capture additional nonlinearities in speech production which should improve the performance of segmentation task. However, to evaluate effectiveness of these segmentation algorithms, we require manual accurate phoneme-level labeled data which is not available for low resource languages such as Gujarati (a low resource language and one of the official languages of India). In order to measure effectiveness of various segmentation algorithms, HMM-based speech synthesis system (HTS) for Gujarati have been built. From the subjective and objective evaluations, it is observed that FD-based features for segmentation work moderately better than other state-of-the-art features such as MFCC, Perceptual Linear Prediction Cepstral Coefficients (PLP-CC), Cochlear Filter Cepstral Coefficients (CFCC), and RelAtive SpecTrAl (RASTA)-based PLP-CC. The Mean Opinion Score (MOS) and the Degraded-MOS, which are the measures of naturalness indicate an improvement of 9.69% with the proposed features from the MFCC (which is found to be the best among the other features) based features.
{"title":"Effectiveness of multiscale fractal dimension-based phonetic segmentation in speech synthesis for low resource language","authors":"Mohammadi Zaki, Nirmesh J. Shah, H. Patil","doi":"10.1109/IALP.2014.6973508","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973508","url":null,"abstract":"Phonetic segmentation plays a key role in developing various speech applications. In this work, we propose to use various features for automatic phonetic segmentation task for forced Viterbi alignment and compare their effectiveness. We propose to use novel multiscale fractal dimension-based features concatenated with Mel-Frequency Cepstral Coefficients (MFCC). The novel features are expected to capture additional nonlinearities in speech production which should improve the performance of segmentation task. However, to evaluate effectiveness of these segmentation algorithms, we require manual accurate phoneme-level labeled data which is not available for low resource languages such as Gujarati (a low resource language and one of the official languages of India). In order to measure effectiveness of various segmentation algorithms, HMM-based speech synthesis system (HTS) for Gujarati have been built. From the subjective and objective evaluations, it is observed that FD-based features for segmentation work moderately better than other state-of-the-art features such as MFCC, Perceptual Linear Prediction Cepstral Coefficients (PLP-CC), Cochlear Filter Cepstral Coefficients (CFCC), and RelAtive SpecTrAl (RASTA)-based PLP-CC. The Mean Opinion Score (MOS) and the Degraded-MOS, which are the measures of naturalness indicate an improvement of 9.69% with the proposed features from the MFCC (which is found to be the best among the other features) based features.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116110483","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973513
Congjun Long, Yiyong Lan, Xiaobing Zhao
In this paper, by using the Tibetan word segmentation system, IEA-TWordSeg, the authors attempt segmentation of the total 1271 sentences in the closed set and 1000 sentences in an open set. The accuracy of testing is 99.54% and 92.41% respectively. The authors describe the wrong segmentation types as well as the causes of the mistakes, and demonstrate the proportion of different types of segmentation errors. The purpose of the article is to provide clues for those who intend to improve the accuracy of Tibetan word segmentation system.
{"title":"The analysis on mistaken segmentation of Tibetan words based on statistical method","authors":"Congjun Long, Yiyong Lan, Xiaobing Zhao","doi":"10.1109/IALP.2014.6973513","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973513","url":null,"abstract":"In this paper, by using the Tibetan word segmentation system, IEA-TWordSeg, the authors attempt segmentation of the total 1271 sentences in the closed set and 1000 sentences in an open set. The accuracy of testing is 99.54% and 92.41% respectively. The authors describe the wrong segmentation types as well as the causes of the mistakes, and demonstrate the proportion of different types of segmentation errors. The purpose of the article is to provide clues for those who intend to improve the accuracy of Tibetan word segmentation system.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"269 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116382833","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973470
Shuqin Shi, Kaihong Yang
Zongshi conjunction word, can be used in different syntax contexts. Zongshi usually directs a simple sentence which has been used as subordinate clause in compound sentence. Zongshi and other adverbs or conjunctions can express different logical relations in compound sentence. The original meaning of Zongshi is resuming or states a fact, which depends on the specific context. The sentence with Zongshi has a strong tendency of subjectivity.
{"title":"The usage of Zongshi","authors":"Shuqin Shi, Kaihong Yang","doi":"10.1109/IALP.2014.6973470","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973470","url":null,"abstract":"Zongshi conjunction word, can be used in different syntax contexts. Zongshi usually directs a simple sentence which has been used as subordinate clause in compound sentence. Zongshi and other adverbs or conjunctions can express different logical relations in compound sentence. The original meaning of Zongshi is resuming or states a fact, which depends on the specific context. The sentence with Zongshi has a strong tendency of subjectivity.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132069642","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973524
Y. Khaw, T. Tan
Alignment of parallel text is a step for building a machine translation. Parallel text alignment is important because linguistic information can be retrieved from the result of alignment which including bilingual dictionaries and grammars correspondence of each language. In this paper, we propose a hybrid approach for standard Malay-dialectal Malay parallel text alignment. The Malay dialects in Malaysia can be grouped according to the states such as Perak dialect, Kedah dialect and Terengganu dialect. It is important to learn Malay dialects as it is still flourished and widely used in many areas especially for unofficial matters. Kelantanese Malay is used as an example for dialectal Malay in this research. The obtained precision and recall values of the proposed alignment methods are above 90%.
{"title":"Hybrid approach for aligning parallel sentences for languages without a written form using standard Malay and Malay dialects","authors":"Y. Khaw, T. Tan","doi":"10.1109/IALP.2014.6973524","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973524","url":null,"abstract":"Alignment of parallel text is a step for building a machine translation. Parallel text alignment is important because linguistic information can be retrieved from the result of alignment which including bilingual dictionaries and grammars correspondence of each language. In this paper, we propose a hybrid approach for standard Malay-dialectal Malay parallel text alignment. The Malay dialects in Malaysia can be grouped according to the states such as Perak dialect, Kedah dialect and Terengganu dialect. It is important to learn Malay dialects as it is still flourished and widely used in many areas especially for unofficial matters. Kelantanese Malay is used as an example for dialectal Malay in this research. The obtained precision and recall values of the proposed alignment methods are above 90%.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134371450","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973472
Quan Zhang, Yi Yuan, Xiangfeng Wei, Zhejie Chi, Peimin Cong, Yihua Du
This paper presents a method for enhancing text classification performance with semantic computing. It adopts conceptual primitives with semantic relations as knowledge expression. Based on the semantic expression, it mined the association relation of primitives among different text classification, and these association rules take association relation as text classification feature. The presented method not only considers what kind of the semantic primitives that a text contains, but also takes account of the association relation of the semantic primitives. Moreover, we test the method with public text classification text set. The experiment result shows that, comparing with the commonly used methods, this method prompts text classification performance.
{"title":"Semantic conceptual primitives computing in text classification","authors":"Quan Zhang, Yi Yuan, Xiangfeng Wei, Zhejie Chi, Peimin Cong, Yihua Du","doi":"10.1109/IALP.2014.6973472","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973472","url":null,"abstract":"This paper presents a method for enhancing text classification performance with semantic computing. It adopts conceptual primitives with semantic relations as knowledge expression. Based on the semantic expression, it mined the association relation of primitives among different text classification, and these association rules take association relation as text classification feature. The presented method not only considers what kind of the semantic primitives that a text contains, but also takes account of the association relation of the semantic primitives. Moreover, we test the method with public text classification text set. The experiment result shows that, comparing with the commonly used methods, this method prompts text classification performance.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"2 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131830068","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973483
Saikrishna Srirampur, Ravi Chandibhamar, Ashish Palakurthi, R. Mamidi
This paper proposes a novel approach to capture the concept1 of an NL query. Given an NL query, the query is mapped to a tagset, which carries the concepts information. The tagset was created by mapping every noun chunk to the attribute of a table (tableName.attributeNarne) and every verb chunk to a relation in the ER schema. The approach is discussed using the Courses Management domain of a University and can be extended to other domains. The tagset here was formed using the ER-schema of the Courses Management Portal of our university. We used the statistical approach to identify the concepts. We ourselves formed a tagged corpus with different types of NL queries. Conditional Random Field algorithm was used for the classification. The results are very promising and are compared to the rule based approach seen in Gupta et al. (2012) [1].
{"title":"Concepts identification of an NL query in NLIDB systems","authors":"Saikrishna Srirampur, Ravi Chandibhamar, Ashish Palakurthi, R. Mamidi","doi":"10.1109/IALP.2014.6973483","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973483","url":null,"abstract":"This paper proposes a novel approach to capture the concept1 of an NL query. Given an NL query, the query is mapped to a tagset, which carries the concepts information. The tagset was created by mapping every noun chunk to the attribute of a table (tableName.attributeNarne) and every verb chunk to a relation in the ER schema. The approach is discussed using the Courses Management domain of a University and can be extended to other domains. The tagset here was formed using the ER-schema of the Courses Management Portal of our university. We used the statistical approach to identify the concepts. We ourselves formed a tagged corpus with different types of NL queries. Conditional Random Field algorithm was used for the classification. The results are very promising and are compared to the rule based approach seen in Gupta et al. (2012) [1].","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131439921","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973502
Josephine E. Petralba
WordNet which is available online and in desktop applications, is an English dictionary where the synonym sets of group of words are linked by means of semantic relations such as hyponymy, meronymy and entailment, among others. The main objective of this paper is to provide the Natural Language Processing (NLP) researchers and Word Game developers with a database such that WordNet content are accessed using simple Structured Query Language (SQL) queries. A distribution copy of Wordnet 3.0 database was downloaded, and loaded into a mySQL database. It was then migrated to Oracle where the database processing to accomplish the objectives of this project was performed. There were 7 tables, 32 materialized views and 4 stored functions constructed. It is at the WordNet dictionary displays that an NLP researcher will initially investigate what Wordnet content he/she needs. Most of the objects were created with reference to the displays. The aim was to come-up with simple SQLs such that the output of an SQL is similar to what is displayed online. Queries to extract content for some Word Games such as HangarooTM and Batang Henyo™ (Genius Child) exemplified the use of this project for Word Games. For Oracle users, distribution copies were made available in a collection of SQL scripts. Non-Oracle users were provided with Excel spreadsheets, Comma Separated Values (CSV) and eXtended Markup Language (XML) files that they can import or load.
{"title":"An extracted database content from WordNet for Natural Language Processing and Word Games","authors":"Josephine E. Petralba","doi":"10.1109/IALP.2014.6973502","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973502","url":null,"abstract":"WordNet which is available online and in desktop applications, is an English dictionary where the synonym sets of group of words are linked by means of semantic relations such as hyponymy, meronymy and entailment, among others. The main objective of this paper is to provide the Natural Language Processing (NLP) researchers and Word Game developers with a database such that WordNet content are accessed using simple Structured Query Language (SQL) queries. A distribution copy of Wordnet 3.0 database was downloaded, and loaded into a mySQL database. It was then migrated to Oracle where the database processing to accomplish the objectives of this project was performed. There were 7 tables, 32 materialized views and 4 stored functions constructed. It is at the WordNet dictionary displays that an NLP researcher will initially investigate what Wordnet content he/she needs. Most of the objects were created with reference to the displays. The aim was to come-up with simple SQLs such that the output of an SQL is similar to what is displayed online. Queries to extract content for some Word Games such as HangarooTM and Batang Henyo™ (Genius Child) exemplified the use of this project for Word Games. For Oracle users, distribution copies were made available in a collection of SQL scripts. Non-Oracle users were provided with Excel spreadsheets, Comma Separated Values (CSV) and eXtended Markup Language (XML) files that they can import or load.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126660200","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973506
Anshu Chittora, H. Patil
In this paper, features extracted from modulation spectrogram are used to classify the phonemes in Gujarati language. Modulation spectrogram which is a 2-dimensional (i.e., 2-D) feature vector, is then reduced to a smaller feature dimension by using the proposed feature extraction method. Gujarati database was manually segmented in 31 phoneme classes. These phonemes are then classified using support vector machine (SVM) classifier. Classification accuracy of phoneme classification is 94.5 % as opposed to classification with the state-of-the-art feature set Mel frequency cepstral coefficients (MFCC), which yields 92.74 % classification accuracy. Classification accuracy for broad phoneme classes, viz., vowel, stops, nasals, semivowels, affricates and fricatives is also determined. Phoneme classification in their respective classes is 95.03 % correct with the proposed feature set. Fusion of MFCC with the proposed feature set is performing even better, giving phoneme classification accuracy of 95.7%. With the fusion of features phoneme classification in sonorant and obstruent classes is found to be 97.01 % accurate.
{"title":"Classification of phonemes using modulation spectrogram based features for Gujarati language","authors":"Anshu Chittora, H. Patil","doi":"10.1109/IALP.2014.6973506","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973506","url":null,"abstract":"In this paper, features extracted from modulation spectrogram are used to classify the phonemes in Gujarati language. Modulation spectrogram which is a 2-dimensional (i.e., 2-D) feature vector, is then reduced to a smaller feature dimension by using the proposed feature extraction method. Gujarati database was manually segmented in 31 phoneme classes. These phonemes are then classified using support vector machine (SVM) classifier. Classification accuracy of phoneme classification is 94.5 % as opposed to classification with the state-of-the-art feature set Mel frequency cepstral coefficients (MFCC), which yields 92.74 % classification accuracy. Classification accuracy for broad phoneme classes, viz., vowel, stops, nasals, semivowels, affricates and fricatives is also determined. Phoneme classification in their respective classes is 95.03 % correct with the proposed feature set. Fusion of MFCC with the proposed feature set is performing even better, giving phoneme classification accuracy of 95.7%. With the fusion of features phoneme classification in sonorant and obstruent classes is found to be 97.01 % accurate.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132623842","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 : 2014-12-04DOI: 10.1109/IALP.2014.6973504
Jing Wang, Yun Zhu, Yaohong Jin
In this paper, a rule-based sentence segmentation system is proposed. We studied the usage and function of Chinese punctuation marks, and classified them into 4 categories. According to whether punctuation can split a sentence, we tagged it with a label SST or un-SST. Experiments were conducted on 4 different kinds of corpus containing 12 kinds of Chinese punctuation marks, and our model achieves a high F-measure over 90% overall. Experiment results show that our approach is effectively for sentence segmentation.
{"title":"A rule-based method for Chinese punctuations processing in sentences segmentation","authors":"Jing Wang, Yun Zhu, Yaohong Jin","doi":"10.1109/IALP.2014.6973504","DOIUrl":"https://doi.org/10.1109/IALP.2014.6973504","url":null,"abstract":"In this paper, a rule-based sentence segmentation system is proposed. We studied the usage and function of Chinese punctuation marks, and classified them into 4 categories. According to whether punctuation can split a sentence, we tagged it with a label SST or un-SST. Experiments were conducted on 4 different kinds of corpus containing 12 kinds of Chinese punctuation marks, and our model achieves a high F-measure over 90% overall. Experiment results show that our approach is effectively for sentence segmentation.","PeriodicalId":117334,"journal":{"name":"2014 International Conference on Asian Language Processing (IALP)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122670833","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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