Pub Date : 2013-12-01DOI: 10.1109/ASRU.2013.6707699
Longlu Qin, Alexander I. Rudnicky
Out-of-vocabulary (OOV) words can appear more than once in a conversation or over a period of time. Such multiple instances of the same OOV word provide valuable information for learning the lexical properties of the word. Therefore, we investigated how to estimate better pronunciation, spelling and part-of-speech (POS) label for recurrent OOV words. We first identified recurrent OOV words from the output of a hybrid decoder by applying a bottom-up clustering approach. Then, multiple instances of the same OOV word were used simultaneously to learn properties of the OOV word. The experimental results showed that the bottom-up clustering approach is very effective at detecting the recurrence of OOV words. Furthermore, by using evidence from multiple instances of the same word, the pronunciation accuracy, recovery rate and POS label accuracy of recurrent OOV words can be substantially improved.
{"title":"Learning better lexical properties for recurrent OOV words","authors":"Longlu Qin, Alexander I. Rudnicky","doi":"10.1109/ASRU.2013.6707699","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707699","url":null,"abstract":"Out-of-vocabulary (OOV) words can appear more than once in a conversation or over a period of time. Such multiple instances of the same OOV word provide valuable information for learning the lexical properties of the word. Therefore, we investigated how to estimate better pronunciation, spelling and part-of-speech (POS) label for recurrent OOV words. We first identified recurrent OOV words from the output of a hybrid decoder by applying a bottom-up clustering approach. Then, multiple instances of the same OOV word were used simultaneously to learn properties of the OOV word. The experimental results showed that the bottom-up clustering approach is very effective at detecting the recurrence of OOV words. Furthermore, by using evidence from multiple instances of the same word, the pronunciation accuracy, recovery rate and POS label accuracy of recurrent OOV words can be substantially improved.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117076308","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707702
Wenlin Zhang, Bi-cheng Li, Weiqiang Zhang
A compact acoustic model for speech recognition is proposed based on nonlinear manifold modeling of the acoustic feature space. Acoustic features of the speech signal is assumed to form a low-dimensional manifold, which is modeled by a mixture of factor analyzers. Each factor analyzer describes a local area of the manifold using a low-dimensional linear model. For an HMM-based speech recognition system, observations of a particular state are constrained to be located on part of the manifold, which may cover several factor analyzers. For each tied-state, a sparse weight vector is obtained through an iteration shrinkage algorithm, in which the sparseness is determined automatically by the training data. For each nonzero component of the weight vector, a low-dimensional factor is estimated for the corresponding factor model according to the maximum a posteriori (MAP) criterion, resulting in a compact state model. Experimental results show that compared with the conventional HMM-GMM system and the SGMM system, the new method not only contains fewer parameters, but also yields better recognition results.
{"title":"Compact acoustic modeling based on acoustic manifold using a mixture of factor analyzers","authors":"Wenlin Zhang, Bi-cheng Li, Weiqiang Zhang","doi":"10.1109/ASRU.2013.6707702","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707702","url":null,"abstract":"A compact acoustic model for speech recognition is proposed based on nonlinear manifold modeling of the acoustic feature space. Acoustic features of the speech signal is assumed to form a low-dimensional manifold, which is modeled by a mixture of factor analyzers. Each factor analyzer describes a local area of the manifold using a low-dimensional linear model. For an HMM-based speech recognition system, observations of a particular state are constrained to be located on part of the manifold, which may cover several factor analyzers. For each tied-state, a sparse weight vector is obtained through an iteration shrinkage algorithm, in which the sparseness is determined automatically by the training data. For each nonzero component of the weight vector, a low-dimensional factor is estimated for the corresponding factor model according to the maximum a posteriori (MAP) criterion, resulting in a compact state model. Experimental results show that compared with the conventional HMM-GMM system and the SGMM system, the new method not only contains fewer parameters, but also yields better recognition results.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129363371","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707770
Roger Hsiao, Tim Ng, F. Grézl, D. Karakos, S. Tsakalidis, L. Nguyen, R. Schwartz
In this paper, we investigate semi-supervised training for low resource languages where the initial systems may have high error rate (≥ 70.0% word eror rate). To handle the lack of data, we study semi-supervised techniques including data selection, data weighting, discriminative training and multilayer perceptron learning to improve system performance. The entire suite of semi-supervised methods presented in this paper was evaluated under the IARPA Babel program for the keyword spotting tasks. Our semi-supervised system had the best performance in the OpenKWS13 surprise language evaluation for the limited condition. In this paper, we describe our work on the Turkish and Vietnamese systems.
{"title":"Discriminative semi-supervised training for keyword search in low resource languages","authors":"Roger Hsiao, Tim Ng, F. Grézl, D. Karakos, S. Tsakalidis, L. Nguyen, R. Schwartz","doi":"10.1109/ASRU.2013.6707770","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707770","url":null,"abstract":"In this paper, we investigate semi-supervised training for low resource languages where the initial systems may have high error rate (≥ 70.0% word eror rate). To handle the lack of data, we study semi-supervised techniques including data selection, data weighting, discriminative training and multilayer perceptron learning to improve system performance. The entire suite of semi-supervised methods presented in this paper was evaluated under the IARPA Babel program for the keyword spotting tasks. Our semi-supervised system had the best performance in the OpenKWS13 surprise language evaluation for the limited condition. In this paper, we describe our work on the Turkish and Vietnamese systems.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127426312","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707774
M. Saraçlar, A. Sethy, B. Ramabhadran, L. Mangu, Jia Cui, Xiaodong Cui, Brian Kingsbury, Jonathan Mamou
Keyword search, in the context of low resource languages, has emerged as a key area of research. The dominant approach in keyword search is to use Automatic Speech Recognition (ASR) as a front end to produce a representation of audio that can be indexed. The biggest drawback of this approach lies in its the inability to deal with out-of-vocabulary words and query terms that are not in the ASR system output. In this paper we present an empirical study evaluating various approaches based on using confusion models as query expansion techniques to address this problem. We present results across four languages using a range of confusion models which lead to significant improvements in keyword search performance as measured by the Maximum Term Weighted Value (MTWV) metric.
{"title":"An empirical study of confusion modeling in keyword search for low resource languages","authors":"M. Saraçlar, A. Sethy, B. Ramabhadran, L. Mangu, Jia Cui, Xiaodong Cui, Brian Kingsbury, Jonathan Mamou","doi":"10.1109/ASRU.2013.6707774","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707774","url":null,"abstract":"Keyword search, in the context of low resource languages, has emerged as a key area of research. The dominant approach in keyword search is to use Automatic Speech Recognition (ASR) as a front end to produce a representation of audio that can be indexed. The biggest drawback of this approach lies in its the inability to deal with out-of-vocabulary words and query terms that are not in the ASR system output. In this paper we present an empirical study evaluating various approaches based on using confusion models as query expansion techniques to address this problem. We present results across four languages using a range of confusion models which lead to significant improvements in keyword search performance as measured by the Maximum Term Weighted Value (MTWV) metric.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124150874","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707700
A. Sethy, Stanley F. Chen, E. Arisoy, B. Ramabhadran, Kartik Audhkhasi, Shrikanth S. Narayanan, Paul Vozila
For many speech recognition tasks, the best language model performance is achieved by collecting text from multiple sources or domains, and interpolating language models built separately on each individual corpus. When multiple corpora are available, it has also been shown that when using a domain adaptation technique such as feature augmentation [1], the performance on each individual domain can be improved by training a joint model across all of the corpora. In this paper, we explore whether improving each domain model via joint training also improves performance when interpolating the models together. We show that the diversity of the individual models is an important consideration, and propose a method for adjusting diversity to optimize overall performance. We present results using word n-gram models and Model M, a class-based n-gram model, and demonstrate improvements in both perplexity and word-error rate relative to state-of-the-art results on a Broadcast News transcription task.
{"title":"Joint training of interpolated exponential n-gram models","authors":"A. Sethy, Stanley F. Chen, E. Arisoy, B. Ramabhadran, Kartik Audhkhasi, Shrikanth S. Narayanan, Paul Vozila","doi":"10.1109/ASRU.2013.6707700","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707700","url":null,"abstract":"For many speech recognition tasks, the best language model performance is achieved by collecting text from multiple sources or domains, and interpolating language models built separately on each individual corpus. When multiple corpora are available, it has also been shown that when using a domain adaptation technique such as feature augmentation [1], the performance on each individual domain can be improved by training a joint model across all of the corpora. In this paper, we explore whether improving each domain model via joint training also improves performance when interpolating the models together. We show that the diversity of the individual models is an important consideration, and propose a method for adjusting diversity to optimize overall performance. We present results using word n-gram models and Model M, a class-based n-gram model, and demonstrate improvements in both perplexity and word-error rate relative to state-of-the-art results on a Broadcast News transcription task.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126690312","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707728
S. Wegmann, Arlo Faria, Adam L. Janin, K. Riedhammer, N. Morgan
In this paper we apply diagnostic analysis to gain a deeper understanding of the performance of the the keyword search system that we have developed for conversational telephone speech in the IARPA Babel program. We summarize the Babel task, its primary performance metric, “actual term weighted value” (ATWV), and our recognition and keyword search systems. Our analysis uses two new oracle ATWV measures, a bootstrap-based ATWV confidence interval, and includes a study of the underpinnings of the large ATWV gains due to system combination. This analysis quantifies the potential ATWV gains from improving the number of true hits and the overall quality of the detection scores in our system's posting lists. It also shows that system combination improves our systems' ATWV via a small increase in the number of true hits in the posting lists.
{"title":"The TAO of ATWV: Probing the mysteries of keyword search performance","authors":"S. Wegmann, Arlo Faria, Adam L. Janin, K. Riedhammer, N. Morgan","doi":"10.1109/ASRU.2013.6707728","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707728","url":null,"abstract":"In this paper we apply diagnostic analysis to gain a deeper understanding of the performance of the the keyword search system that we have developed for conversational telephone speech in the IARPA Babel program. We summarize the Babel task, its primary performance metric, “actual term weighted value” (ATWV), and our recognition and keyword search systems. Our analysis uses two new oracle ATWV measures, a bootstrap-based ATWV confidence interval, and includes a study of the underpinnings of the large ATWV gains due to system combination. This analysis quantifies the potential ATWV gains from improving the number of true hits and the overall quality of the detection scores in our system's posting lists. It also shows that system combination improves our systems' ATWV via a small increase in the number of true hits in the posting lists.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"87 9 Suppl 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132658268","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707757
Raymond Brueckner, Björn Schuller
With the impressive advances of deep learning in recent years the interest in neural networks has resurged in the fields of automatic speech recognition and emotion recognition. In this paper we apply neural networks to address speaker-independent detection and classification of laughter and filler vocalizations in speech. We first explore modeling class posteriors with standard neural networks and deep stacked autoencoders. Then, we adopt a hierarchical neural architecture to compute enhanced class posteriors and demonstrate that this approach introduces significant and consistent improvements on the Social Signals Sub-Challenge of the Interspeech 2013 Computational Paralinguistics Challenge (ComParE). On this task we achieve a value of 92.4% of the unweighted average area-under-the-curve, which is the official competition measure, on the test set. This constitutes an improvement of 9.1% over the baseline and is the best result obtained so far on this task.
{"title":"Hierarchical neural networks and enhanced class posteriors for social signal classification","authors":"Raymond Brueckner, Björn Schuller","doi":"10.1109/ASRU.2013.6707757","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707757","url":null,"abstract":"With the impressive advances of deep learning in recent years the interest in neural networks has resurged in the fields of automatic speech recognition and emotion recognition. In this paper we apply neural networks to address speaker-independent detection and classification of laughter and filler vocalizations in speech. We first explore modeling class posteriors with standard neural networks and deep stacked autoencoders. Then, we adopt a hierarchical neural architecture to compute enhanced class posteriors and demonstrate that this approach introduces significant and consistent improvements on the Social Signals Sub-Challenge of the Interspeech 2013 Computational Paralinguistics Challenge (ComParE). On this task we achieve a value of 92.4% of the unweighted average area-under-the-curve, which is the official competition measure, on the test set. This constitutes an improvement of 9.1% over the baseline and is the best result obtained so far on this task.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130663355","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707763
Yajie Miao, Florian Metze, Shourabh Rawat
As a feed-forward architecture, the recently proposed maxout networks integrate dropout naturally and show state-of-the-art results on various computer vision datasets. This paper investigates the application of deep maxout networks (DMNs) to large vocabulary continuous speech recognition (LVCSR) tasks. Our focus is on the particular advantage of DMNs under low-resource conditions with limited transcribed speech. We extend DMNs to hybrid and bottleneck feature systems, and explore optimal network structures (number of maxout layers, pooling strategy, etc) for both setups. On the newly released Babel corpus, behaviors of DMNs are extensively studied under different levels of data availability. Experiments show that DMNs improve low-resource speech recognition significantly. Moreover, DMNs introduce sparsity to their hidden activations and thus can act as sparse feature extractors.
{"title":"Deep maxout networks for low-resource speech recognition","authors":"Yajie Miao, Florian Metze, Shourabh Rawat","doi":"10.1109/ASRU.2013.6707763","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707763","url":null,"abstract":"As a feed-forward architecture, the recently proposed maxout networks integrate dropout naturally and show state-of-the-art results on various computer vision datasets. This paper investigates the application of deep maxout networks (DMNs) to large vocabulary continuous speech recognition (LVCSR) tasks. Our focus is on the particular advantage of DMNs under low-resource conditions with limited transcribed speech. We extend DMNs to hybrid and bottleneck feature systems, and explore optimal network structures (number of maxout layers, pooling strategy, etc) for both setups. On the newly released Babel corpus, behaviors of DMNs are extensively studied under different levels of data availability. Experiments show that DMNs improve low-resource speech recognition significantly. Moreover, DMNs introduce sparsity to their hidden activations and thus can act as sparse feature extractors.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133457477","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707775
F. Grézl, M. Karafiát
This paper presents bootstrapping approach for neural network training. The neural networks serve as bottle-neck feature extractor for subsequent GMM-HMM recognizer. The recognizer is also used for transcription and confidence assignment of untranscribed data. Based on the confidence, segments are selected and mixed with supervised data and new NNs are trained. With this approach, it is possible to recover 40-55% of the difference between partially and fully transcribed data (3 to 5% absolute improvement over NN trained on supervised data only). Using 70-85% of automatically transcribed segments with the highest confidence was found optimal to achieve this result.
{"title":"Semi-supervised bootstrapping approach for neural network feature extractor training","authors":"F. Grézl, M. Karafiát","doi":"10.1109/ASRU.2013.6707775","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707775","url":null,"abstract":"This paper presents bootstrapping approach for neural network training. The neural networks serve as bottle-neck feature extractor for subsequent GMM-HMM recognizer. The recognizer is also used for transcription and confidence assignment of untranscribed data. Based on the confidence, segments are selected and mixed with supervised data and new NNs are trained. With this approach, it is possible to recover 40-55% of the difference between partially and fully transcribed data (3 to 5% absolute improvement over NN trained on supervised data only). Using 70-85% of automatically transcribed segments with the highest confidence was found optimal to achieve this result.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133392264","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 : 2013-12-01DOI: 10.1109/ASRU.2013.6707771
Ramya Rasipuram, Marzieh Razavi, M. Magimai.-Doss
Standard automatic speech recognition (ASR) systems rely on transcribed speech, language models, and pronunciation dictionaries to achieve state-of-the-art performance. The unavailability of these resources constrains the ASR technology to be available for many languages. In this paper, we propose a novel zero-resourced ASR approach to train acoustic models that only uses list of probable words from the language of interest. The proposed approach is based on Kullback-Leibler divergence based hidden Markov model (KL-HMM), grapheme subword units, knowledge of grapheme-to-phoneme mapping, and graphemic constraints derived from the word list. The approach also exploits existing acoustic and lexical resources available in other resource rich languages. Furthermore, we propose unsupervised adaptation of KL-HMM acoustic model parameters if untranscribed speech data in the target language is available. We demonstrate the potential of the proposed approach through a simulated study on Greek language.
{"title":"Probabilistic lexical modeling and unsupervised training for zero-resourced ASR","authors":"Ramya Rasipuram, Marzieh Razavi, M. Magimai.-Doss","doi":"10.1109/ASRU.2013.6707771","DOIUrl":"https://doi.org/10.1109/ASRU.2013.6707771","url":null,"abstract":"Standard automatic speech recognition (ASR) systems rely on transcribed speech, language models, and pronunciation dictionaries to achieve state-of-the-art performance. The unavailability of these resources constrains the ASR technology to be available for many languages. In this paper, we propose a novel zero-resourced ASR approach to train acoustic models that only uses list of probable words from the language of interest. The proposed approach is based on Kullback-Leibler divergence based hidden Markov model (KL-HMM), grapheme subword units, knowledge of grapheme-to-phoneme mapping, and graphemic constraints derived from the word list. The approach also exploits existing acoustic and lexical resources available in other resource rich languages. Furthermore, we propose unsupervised adaptation of KL-HMM acoustic model parameters if untranscribed speech data in the target language is available. We demonstrate the potential of the proposed approach through a simulated study on Greek language.","PeriodicalId":265258,"journal":{"name":"2013 IEEE Workshop on Automatic Speech Recognition and Understanding","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133914607","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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