Pub Date : 2008-12-01DOI: 10.1109/SLT.2008.4777836
M. Kockmann, L. Burget
In this paper we use acoustic and prosodic features jointly in a long-temporal lexical context for automatic speaker recognition from speech. The contours of pitch, energy and cepstral coefficients are continuously modeled over the time span of a syllable to capture the speaking style on phonetic level. As these features are affected by session variability, established channel compensation techniques are examined. Results for the combination of different features on a syllable-level as well as for channel compensation are presented for the NIST SRE 2006 speaker identification task. To show the complementary character of the features, the proposed system is fused with an acoustic short-time system, leading to a relative improvement of 10.4%.
{"title":"Contour modeling of prosodic and acoustic features for speaker recognition","authors":"M. Kockmann, L. Burget","doi":"10.1109/SLT.2008.4777836","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777836","url":null,"abstract":"In this paper we use acoustic and prosodic features jointly in a long-temporal lexical context for automatic speaker recognition from speech. The contours of pitch, energy and cepstral coefficients are continuously modeled over the time span of a syllable to capture the speaking style on phonetic level. As these features are affected by session variability, established channel compensation techniques are examined. Results for the combination of different features on a syllable-level as well as for channel compensation are presented for the NIST SRE 2006 speaker identification task. To show the complementary character of the features, the proposed system is fused with an acoustic short-time system, leading to a relative improvement of 10.4%.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116717717","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777903
Manish Gaurav
In this paper, we study the performance of different prosody and spectral features of speech on an emotion detection task. In particular, a feature selection algorithm has been used to assess the relevancy of the different features. Gaussian mixtures models have been used to model the features extracted at the frame-level, while support vector machines (SVM) and k-nearest neighbor (k-NN) methods have been used to model the features extracted at the utterance level. We use a normalization approach (T-norm) to combine the scores from the different models. The results using the above approach are reported for the Berlin emotional database corpus and the task consisted of classifying the six emotions namely - anger, happiness, neutral, sadness, boredom and anxiety. We show that the use of feature selection algorithm improves the result, while in addition the fusion of GMM and SVM results in an overall accuracy of 75.4% for the above task.
{"title":"Performance analysis of spectral and prosodic features and their fusion for emotion recognition in speech","authors":"Manish Gaurav","doi":"10.1109/SLT.2008.4777903","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777903","url":null,"abstract":"In this paper, we study the performance of different prosody and spectral features of speech on an emotion detection task. In particular, a feature selection algorithm has been used to assess the relevancy of the different features. Gaussian mixtures models have been used to model the features extracted at the frame-level, while support vector machines (SVM) and k-nearest neighbor (k-NN) methods have been used to model the features extracted at the utterance level. We use a normalization approach (T-norm) to combine the scores from the different models. The results using the above approach are reported for the Berlin emotional database corpus and the task consisted of classifying the six emotions namely - anger, happiness, neutral, sadness, boredom and anxiety. We show that the use of feature selection algorithm improves the result, while in addition the fusion of GMM and SVM results in an overall accuracy of 75.4% for the above task.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127151341","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777875
Jen-Tzung Chien, C. Chueh
Latent Dirichlet allocation (LDA) has been successfully presented for document modeling and classification. LDA calculates the document probability based on bag-of-words scheme without considering the sequence of words. This model discovers the topic structure at document level, which is different from the concern of word prediction in speech recognition. In this paper, we present a new latent Dirichlet language model (LDLM) for modeling of word sequence. A new Bayesian framework is introduced by merging the Dirichlet priors to characterize the uncertainty of latent topics of n-gram events. The robust topic-based language model is established accordingly. In the experiments, we implement LDLM for continuous speech recognition and obtain better performance than probabilistic latent semantic analysis (PLSA) based language method.
{"title":"Latent dirichlet language model for speech recognition","authors":"Jen-Tzung Chien, C. Chueh","doi":"10.1109/SLT.2008.4777875","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777875","url":null,"abstract":"Latent Dirichlet allocation (LDA) has been successfully presented for document modeling and classification. LDA calculates the document probability based on bag-of-words scheme without considering the sequence of words. This model discovers the topic structure at document level, which is different from the concern of word prediction in speech recognition. In this paper, we present a new latent Dirichlet language model (LDLM) for modeling of word sequence. A new Bayesian framework is introduced by merging the Dirichlet priors to characterize the uncertainty of latent topics of n-gram events. The robust topic-based language model is established accordingly. In the experiments, we implement LDLM for continuous speech recognition and obtain better performance than probabilistic latent semantic analysis (PLSA) based language method.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114462496","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777894
Fernando Batista, R. Amaral, I. Trancoso, N. Mamede
The application of speech recognition to live subtitling of Broadcast News has motivated the adaptation of the lexical and language models of the recognizer on a daily basis with text material retrieved from online newspapers. This paper studies the impact of this adaptation on two of the blocks following the speech recognition module: capitalization and topic indexation. We describe and evaluate different adaptation approaches that try to explore the language dynamics.
{"title":"Impact of dynamic model adaptation beyond speech recognition","authors":"Fernando Batista, R. Amaral, I. Trancoso, N. Mamede","doi":"10.1109/SLT.2008.4777894","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777894","url":null,"abstract":"The application of speech recognition to live subtitling of Broadcast News has motivated the adaptation of the lexical and language models of the recognizer on a daily basis with text material retrieved from online newspapers. This paper studies the impact of this adaptation on two of the blocks following the speech recognition module: capitalization and topic indexation. We describe and evaluate different adaptation approaches that try to explore the language dynamics.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"515 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132754505","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777878
Richard Dufour, Y. Estève
Automatic speech recognition (ASR) systems are used in a large number of applications, in spite of the inevitable recognition errors. In this study we propose a pragmatic approach to automatically repair ASR outputs by taking into account linguistic and acoustic information, using formal rules or stochastic methods. The proposed strategy consists in developing a specific correction solution for each specific kind of errors. In this paper, we apply this strategy on two case studies specific to French language. We show that it is possible, on automatic transcriptions of French broadcast news, to decrease the error rate of a specific error by 11.4% in one of two the case studies, and 86.4% in the other one. These results are encouraging and show the interest of developing more specific solutions to cover a wider set of errors in a future work.
{"title":"Correcting asr outputs: Specific solutions to specific errors in French","authors":"Richard Dufour, Y. Estève","doi":"10.1109/SLT.2008.4777878","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777878","url":null,"abstract":"Automatic speech recognition (ASR) systems are used in a large number of applications, in spite of the inevitable recognition errors. In this study we propose a pragmatic approach to automatically repair ASR outputs by taking into account linguistic and acoustic information, using formal rules or stochastic methods. The proposed strategy consists in developing a specific correction solution for each specific kind of errors. In this paper, we apply this strategy on two case studies specific to French language. We show that it is possible, on automatic transcriptions of French broadcast news, to decrease the error rate of a specific error by 11.4% in one of two the case studies, and 86.4% in the other one. These results are encouraging and show the interest of developing more specific solutions to cover a wider set of errors in a future work.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128957470","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 : 2008-12-01DOI: 10.1109/slt.2008.4777870
Fei Liu, Feifan Liu, Yang Liu
{"title":"Automatic keyword extraction for the meeting corpus using supervised approach and bigram expansion","authors":"Fei Liu, Feifan Liu, Yang Liu","doi":"10.1109/slt.2008.4777870","DOIUrl":"https://doi.org/10.1109/slt.2008.4777870","url":null,"abstract":"","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"28 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116859026","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777873
C. Chueh, Jen-Tzung Chien
Continuous representation of word sequence can effectively solve data sparseness problem in n-gram language model, where the discrete variables of words are represented and the unseen events are prone to happen. This problem is increasingly severe when extracting long-distance regularities for high-order n-gram model. Rather than considering discrete word space, we construct the continuous space of word sequence where the latent topic information is extracted. The continuous vector is formed by the topic posterior probabilities and the least-squares projection matrix from discrete word space to continuous topic space is estimated accordingly. The unseen words can be predicted through the new continuous latent topic language model. In the experiments on continuous speech recognition, we obtain significant performance improvement over the conventional topic-based language model.
{"title":"Continuous topic language modeling for speech recognition","authors":"C. Chueh, Jen-Tzung Chien","doi":"10.1109/SLT.2008.4777873","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777873","url":null,"abstract":"Continuous representation of word sequence can effectively solve data sparseness problem in n-gram language model, where the discrete variables of words are represented and the unseen events are prone to happen. This problem is increasingly severe when extracting long-distance regularities for high-order n-gram model. Rather than considering discrete word space, we construct the continuous space of word sequence where the latent topic information is extracted. The continuous vector is formed by the topic posterior probabilities and the least-squares projection matrix from discrete word space to continuous topic space is estimated accordingly. The unseen words can be predicted through the new continuous latent topic language model. In the experiments on continuous speech recognition, we obtain significant performance improvement over the conventional topic-based language model.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116273104","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777862
M. Sawaki, Yasuhiro Minami, Ryuichiro Higashinaka, Kohji Dohsaka, Eisaku Maeda
In order to design a dialogue system that users enjoy and want to be near for a long time, it is important to know the effect of the system's action on users. This paper describes ldquoWho is thisrdquo quiz dialogue system and its users' evaluation. Its quiz-style information presentation has been found effective for educational tasks. In our ongoing effort to make it closer to a conversational partner, we implemented the system as a stuffed-toy (or CG equivalent). Quizzes are automatically generated from Wikipedia articles, rather than from hand-crafted sets of biographical facts. Network mining is utilized to prepare adaptive system responses. Experiments showed the effectiveness of person network and the relationship of user attribute and interest level.
{"title":"“Who is this” quiz dialogue system and users' evaluation","authors":"M. Sawaki, Yasuhiro Minami, Ryuichiro Higashinaka, Kohji Dohsaka, Eisaku Maeda","doi":"10.1109/SLT.2008.4777862","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777862","url":null,"abstract":"In order to design a dialogue system that users enjoy and want to be near for a long time, it is important to know the effect of the system's action on users. This paper describes ldquoWho is thisrdquo quiz dialogue system and its users' evaluation. Its quiz-style information presentation has been found effective for educational tasks. In our ongoing effort to make it closer to a conversational partner, we implemented the system as a stuffed-toy (or CG equivalent). Quizzes are automatically generated from Wikipedia articles, rather than from hand-crafted sets of biographical facts. Network mining is utilized to prepare adaptive system responses. Experiments showed the effectiveness of person network and the relationship of user attribute and interest level.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114451000","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777857
Keelan Evanini, P. Hunter, J. Liscombe, David Suendermann-Oeft, K. Dayanidhi, R. Pieraccini
In this paper we introduce a subjective metric for evaluating the performance of spoken dialog systems, caller experience (CE). CE is a useful metric for tracking the overall performance of a system in deployment, as well as for isolating individual problematic calls in which the system underperforms. The proposed CE metric differs from most performance evaluation metrics proposed in the past in that it is a) a subjective, qualitative rating of the call, and b) provided by expert, external listeners, not the callers themselves. The results of an experiment in which a set of human experts listened to the same calls three times are presented. The fact that these results show a high level of agreement among different listeners, despite the subjective nature of the task, demonstrates the validity of using CE as a standard metric. Finally, an automated rating system using objective measures is shown to perform at the same high level as the humans. This is an important advance, since it provides a way to reduce the human labor costs associated with producing a reliable CE.
{"title":"Caller Experience: A method for evaluating dialog systems and its automatic prediction","authors":"Keelan Evanini, P. Hunter, J. Liscombe, David Suendermann-Oeft, K. Dayanidhi, R. Pieraccini","doi":"10.1109/SLT.2008.4777857","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777857","url":null,"abstract":"In this paper we introduce a subjective metric for evaluating the performance of spoken dialog systems, caller experience (CE). CE is a useful metric for tracking the overall performance of a system in deployment, as well as for isolating individual problematic calls in which the system underperforms. The proposed CE metric differs from most performance evaluation metrics proposed in the past in that it is a) a subjective, qualitative rating of the call, and b) provided by expert, external listeners, not the callers themselves. The results of an experiment in which a set of human experts listened to the same calls three times are presented. The fact that these results show a high level of agreement among different listeners, despite the subjective nature of the task, demonstrates the validity of using CE as a standard metric. Finally, an automated rating system using objective measures is shown to perform at the same high level as the humans. This is an important advance, since it provides a way to reduce the human labor costs associated with producing a reliable CE.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127400180","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 : 2008-12-01DOI: 10.1109/SLT.2008.4777861
Filipe M. Martins, Joana Paulo Pardal, Luís Franqueira, Pedro Arez, N. Mamede
This paper presents a system that helps you cook a recipe through a spoken dialogue tutoring session. We report our experience while creating the first version of a tutoring dialogue system that helps the user cook a selected dish. Having a working framework to support us with the creation of the cooking assistant, the main challenge we faced was the change of paradigm: instead of the system being driven by the user, the user is instructed by the system. The result is a system capable of dictating generic contents to the user. On top of it, the system can be used in several domains where the goal is not the replacement of the user but providing some assistance while (s)he performs some procedural task.
{"title":"Starting to cook a tutoring dialogue system","authors":"Filipe M. Martins, Joana Paulo Pardal, Luís Franqueira, Pedro Arez, N. Mamede","doi":"10.1109/SLT.2008.4777861","DOIUrl":"https://doi.org/10.1109/SLT.2008.4777861","url":null,"abstract":"This paper presents a system that helps you cook a recipe through a spoken dialogue tutoring session. We report our experience while creating the first version of a tutoring dialogue system that helps the user cook a selected dish. Having a working framework to support us with the creation of the cooking assistant, the main challenge we faced was the change of paradigm: instead of the system being driven by the user, the user is instructed by the system. The result is a system capable of dictating generic contents to the user. On top of it, the system can be used in several domains where the goal is not the replacement of the user but providing some assistance while (s)he performs some procedural task.","PeriodicalId":186876,"journal":{"name":"2008 IEEE Spoken Language Technology Workshop","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124710626","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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