Pub Date : 2009-12-01DOI: 10.1109/ASRU.2009.5373314
Barbara Schuppler, Joost van Doremalen, O. Scharenborg, B. Cranen, L. Boves
This paper combines acoustic features with a high temporal and a high frequency resolution to reliably classify articulatory events of short duration, such as bursts in plosives. SVM classification experiments on TIMIT and SVArticulatory showed that articulatory-acoustic features (AFs) based on a combination of MFCCs derived from a long window of 25ms and a short window of 5ms that are both shifted with 2.5ms steps (Both) outperform standard MFCCs derived with a window of 25 ms and a shift of 10 ms (Baseline). Finally, comparison of the TIMIT and SVArticulatory results showed that for classifiers trained on data that allows for asynchronously changing AFs (SVArticulatory) the improvement from Baseline to Both is larger than for classifiers trained on data where AFs change simultaneously with the phone boundaries (TIMIT).
{"title":"Using temporal information for improving articulatory-acoustic feature classification","authors":"Barbara Schuppler, Joost van Doremalen, O. Scharenborg, B. Cranen, L. Boves","doi":"10.1109/ASRU.2009.5373314","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373314","url":null,"abstract":"This paper combines acoustic features with a high temporal and a high frequency resolution to reliably classify articulatory events of short duration, such as bursts in plosives. SVM classification experiments on TIMIT and SVArticulatory showed that articulatory-acoustic features (AFs) based on a combination of MFCCs derived from a long window of 25ms and a short window of 5ms that are both shifted with 2.5ms steps (Both) outperform standard MFCCs derived with a window of 25 ms and a shift of 10 ms (Baseline). Finally, comparison of the TIMIT and SVArticulatory results showed that for classifiers trained on data that allows for asynchronously changing AFs (SVArticulatory) the improvement from Baseline to Both is larger than for classifiers trained on data where AFs change simultaneously with the phone boundaries (TIMIT).","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131033030","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373332
H. Veisi, H. Sameti
In this paper a novel method, called PC-PMC, is proposed to improve the performance of automatic speech recognition systems in noisy environments. This method is based on the parallel model combination (PMC) technique and uses the Cepstral Mean Subtraction (CMS) normalization ability and Principal Component Analysis (PCA) compression and de-correlation capabilities. It takes the advantages of both additive noise compensation of PMC and convolutive noise removal ability of CMS and PCA. The first problem to be solved in the realizing of PC-PMC is that PMC algorithm requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Also, it is required to design a framework for adaptation of the PCA transform in the presence of noise. The method proposed in this paper provides solutions to the both problems. Our evaluations are done on four different real noisy tasks using Nevisa Persian continuous speech recognition system. Experimental results demonstrate significant reduction in word error rate using PC-PMC in comparison with the standard robustness methods.
{"title":"An improved parallel model combination method for noisy speech recognition","authors":"H. Veisi, H. Sameti","doi":"10.1109/ASRU.2009.5373332","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373332","url":null,"abstract":"In this paper a novel method, called PC-PMC, is proposed to improve the performance of automatic speech recognition systems in noisy environments. This method is based on the parallel model combination (PMC) technique and uses the Cepstral Mean Subtraction (CMS) normalization ability and Principal Component Analysis (PCA) compression and de-correlation capabilities. It takes the advantages of both additive noise compensation of PMC and convolutive noise removal ability of CMS and PCA. The first problem to be solved in the realizing of PC-PMC is that PMC algorithm requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Also, it is required to design a framework for adaptation of the PCA transform in the presence of noise. The method proposed in this paper provides solutions to the both problems. Our evaluations are done on four different real noisy tasks using Nevisa Persian continuous speech recognition system. Experimental results demonstrate significant reduction in word error rate using PC-PMC in comparison with the standard robustness methods.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130933593","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373362
Simon Wiesler, M. Nußbaum-Thom, G. Heigold, R. Schlüter, H. Ney
Hidden Markov Models with Gaussian Mixture Models as emission probabilities (GHMMs) are the underlying structure of all state-of-the-art speech recognition systems. Using Gaussian mixture distributions follows the generative approach where the class-conditional probability is modeled, although for classification only the posterior probability is needed. Though being very successful in related tasks like Natural Language Processing (NLP), in speech recognition direct modeling of posterior probabilities with log-linear models has rarely been used and has not been applied successfully to continuous speech recognition. In this paper we report competitive results for a speech recognizer with a log-linear acoustic model on the Wall Street Journal corpus, a Large Vocabulary Continuous Speech Recognition (LVCSR) task. We trained this model from scratch, i.e. without relying on an existing GHMM system. Previously the use of data dependent sparse features for log-linear models has been proposed. We compare them with polynomial features and show that the combination of polynomial and data dependent sparse features leads to better results.
{"title":"Investigations on features for log-linear acoustic models in continuous speech recognition","authors":"Simon Wiesler, M. Nußbaum-Thom, G. Heigold, R. Schlüter, H. Ney","doi":"10.1109/ASRU.2009.5373362","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373362","url":null,"abstract":"Hidden Markov Models with Gaussian Mixture Models as emission probabilities (GHMMs) are the underlying structure of all state-of-the-art speech recognition systems. Using Gaussian mixture distributions follows the generative approach where the class-conditional probability is modeled, although for classification only the posterior probability is needed. Though being very successful in related tasks like Natural Language Processing (NLP), in speech recognition direct modeling of posterior probabilities with log-linear models has rarely been used and has not been applied successfully to continuous speech recognition. In this paper we report competitive results for a speech recognizer with a log-linear acoustic model on the Wall Street Journal corpus, a Large Vocabulary Continuous Speech Recognition (LVCSR) task. We trained this model from scratch, i.e. without relying on an existing GHMM system. Previously the use of data dependent sparse features for log-linear models has been proposed. We compare them with polynomial features and show that the combination of polynomial and data dependent sparse features leads to better results.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125881899","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373389
J. Huang, Xi Zhou, M. Hasegawa-Johnson, Thomas S. Huang
While a sound spoken is described by a handful of frame-level spectral vectors, not all frames have equal contribution for either human perception or machine classification. In this paper, we introduce a novel framework to automatically emphasize important speech frames relevant to phonetic information. We jointly learn the importance of speech frames by a distance metric across the phone classes, attempting to satisfy a large margin constraint: the distance from a segment to its correct label class should be less than the distance to any other phone class by the largest possible margin. Furthermore, an universal background model structure is proposed to give the correspondence between statistical models of phone types and tokens, allowing us to use statistical models of each phone token in a large margin speech recognition framework. Experiments on TIMIT database demonstrated the effectiveness of our framework.
{"title":"Kernel metric learning for phonetic classification","authors":"J. Huang, Xi Zhou, M. Hasegawa-Johnson, Thomas S. Huang","doi":"10.1109/ASRU.2009.5373389","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373389","url":null,"abstract":"While a sound spoken is described by a handful of frame-level spectral vectors, not all frames have equal contribution for either human perception or machine classification. In this paper, we introduce a novel framework to automatically emphasize important speech frames relevant to phonetic information. We jointly learn the importance of speech frames by a distance metric across the phone classes, attempting to satisfy a large margin constraint: the distance from a segment to its correct label class should be less than the distance to any other phone class by the largest possible margin. Furthermore, an universal background model structure is proposed to give the correspondence between statistical models of phone types and tokens, allowing us to use statistical models of each phone token in a large margin speech recognition framework. Experiments on TIMIT database demonstrated the effectiveness of our framework.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126272397","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373365
Wenzhu Shen, Roger Peng Yu, F. Seide, Ji Wu
Automatic generation of punctuation is an essential feature for many speech-to-text transcription tasks. This paper describes a Maximum A-Posteriori (MAP) approach for inserting punctuation marks into raw word sequences obtained from Automatic Speech Recognition (ASR). The system consists of an “acoustic model” (AM) for prosodic features (actually pause duration) and a “language model” (LM) for text-only features. The LM combines three components: an MLP-based trigger-word model and a forward and a backward trigram punctuation predictor. The separation into acoustic and language model allows to learn these models on different corpora, especially allowing the LM to be trained on large amounts of data (text) for which no acoustic information is available. We find that the trigger-word LM is very useful, and further improvement can be achieved when combining both prosodic and lexical information. We achieve an F-measure of 81.0% and 56.5% for voicemails and podcasts, respectively, on reference transcripts, and 69.6% for voicemails on ASR transcripts.
{"title":"Automatic punctuation generation for speech","authors":"Wenzhu Shen, Roger Peng Yu, F. Seide, Ji Wu","doi":"10.1109/ASRU.2009.5373365","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373365","url":null,"abstract":"Automatic generation of punctuation is an essential feature for many speech-to-text transcription tasks. This paper describes a Maximum A-Posteriori (MAP) approach for inserting punctuation marks into raw word sequences obtained from Automatic Speech Recognition (ASR). The system consists of an “acoustic model” (AM) for prosodic features (actually pause duration) and a “language model” (LM) for text-only features. The LM combines three components: an MLP-based trigger-word model and a forward and a backward trigram punctuation predictor. The separation into acoustic and language model allows to learn these models on different corpora, especially allowing the LM to be trained on large amounts of data (text) for which no acoustic information is available. We find that the trigger-word LM is very useful, and further improvement can be achieved when combining both prosodic and lexical information. We achieve an F-measure of 81.0% and 56.5% for voicemails and podcasts, respectively, on reference transcripts, and 69.6% for voicemails on ASR transcripts.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114277104","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373338
A. Rastrow, A. Sethy, B. Ramabhadran
In this paper, we present a novel version of discriminative training for N-gram language models. Language models impose language specific constraints on the acoustic hypothesis and are crucial in discriminating between competing acoustic hypotheses. As reported in the literature, discriminative training of acoustic models has yielded significant improvements in the performance of a speech recognition system, however, discriminative training for N-gram language models (LMs) has not yielded the same impact. In this paper, we present three techniques to improve the discriminative training of LMs, namely updating the back-off probability of unseen events, normalization of the N-gram updates to ensure a probability distribution and a relative-entropy based global constraint on the N-gram probability updates. We also present a framework for discriminative adaptation of LMs to a new domain and compare it to existing linear interpolation methods. Results are reported on the Broadcast News and the MIT lecture corpora. A modest improvement of 0.2% absolute (on Broadcast News) and 0.3% absolute (on MIT lectures) was observed with discriminatively trained LMs over state-of-the-art systems.
{"title":"Constrained discriminative training of N-gram language models","authors":"A. Rastrow, A. Sethy, B. Ramabhadran","doi":"10.1109/ASRU.2009.5373338","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373338","url":null,"abstract":"In this paper, we present a novel version of discriminative training for N-gram language models. Language models impose language specific constraints on the acoustic hypothesis and are crucial in discriminating between competing acoustic hypotheses. As reported in the literature, discriminative training of acoustic models has yielded significant improvements in the performance of a speech recognition system, however, discriminative training for N-gram language models (LMs) has not yielded the same impact. In this paper, we present three techniques to improve the discriminative training of LMs, namely updating the back-off probability of unseen events, normalization of the N-gram updates to ensure a probability distribution and a relative-entropy based global constraint on the N-gram probability updates. We also present a framework for discriminative adaptation of LMs to a new domain and compare it to existing linear interpolation methods. Results are reported on the Broadcast News and the MIT lecture corpora. A modest improvement of 0.2% absolute (on Broadcast News) and 0.3% absolute (on MIT lectures) was observed with discriminatively trained LMs over state-of-the-art systems.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461206","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373544
M. Wöllmer, F. Eyben, Björn Schuller, G. Rigoll
This paper introduces a novel graphical model architecture for robust and vocabulary independent keyword spotting which does not require the training of an explicit garbage model. We show how a graphical model structure for phoneme recognition can be extended to a keyword spotter that is robust with respect to phoneme recognition errors. We use a hidden garbage variable together with the concept of switching parents to model keywords as well as arbitrary speech. This implies that keywords can be added to the vocabulary without having to re-train the model. Thereby the design of our model architecture is optimised to reliably detect keywords rather than to decode keyword phoneme sequences as arbitrary speech, while offering a parameter to adjust the operating point on the receiver operating characteristics curve. Experiments on the TIMIT corpus reveal that our graphical model outperforms a comparable hidden Markov model based keyword spotter that uses conventional garbage modelling.
{"title":"Robust vocabulary independent keyword spotting with graphical models","authors":"M. Wöllmer, F. Eyben, Björn Schuller, G. Rigoll","doi":"10.1109/ASRU.2009.5373544","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373544","url":null,"abstract":"This paper introduces a novel graphical model architecture for robust and vocabulary independent keyword spotting which does not require the training of an explicit garbage model. We show how a graphical model structure for phoneme recognition can be extended to a keyword spotter that is robust with respect to phoneme recognition errors. We use a hidden garbage variable together with the concept of switching parents to model keywords as well as arbitrary speech. This implies that keywords can be added to the vocabulary without having to re-train the model. Thereby the design of our model architecture is optimised to reliably detect keywords rather than to decode keyword phoneme sequences as arbitrary speech, while offering a parameter to adjust the operating point on the receiver operating characteristics curve. Experiments on the TIMIT corpus reveal that our graphical model outperforms a comparable hidden Markov model based keyword spotter that uses conventional garbage modelling.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125197866","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373347
K. Vertanen, P. Kristensson
We investigate how to automatically align spoken corrections with an initial speech recognition result. Such automatic alignment would enable one-step voice-only correction in which users simply respeak their intended text. We present three new models for automatically aligning corrections: a 1-best model, a word confusion network model, and a revision model. The revision model allows users to alter what they intended to write even when the initial recognition was completely correct. We evaluate our models with data gathered from two user studies. We show that providing just a single correct word of context dramatically improves alignment success from 65% to 84%. We find that a majority of users provide such context without being explicitly instructed to do so. We find that the revision model is superior when users modify words in their initial recognition, improving alignment success from 73% to 83%. We show how our models can easily incorporate prior information about correction location and we show that such information aids alignment success. Last, we observe that users speak their intended text faster and with fewer re-recordings than if they are forced to speak misrecognized text.
{"title":"Automatic selection of recognition errors by respeaking the intended text","authors":"K. Vertanen, P. Kristensson","doi":"10.1109/ASRU.2009.5373347","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373347","url":null,"abstract":"We investigate how to automatically align spoken corrections with an initial speech recognition result. Such automatic alignment would enable one-step voice-only correction in which users simply respeak their intended text. We present three new models for automatically aligning corrections: a 1-best model, a word confusion network model, and a revision model. The revision model allows users to alter what they intended to write even when the initial recognition was completely correct. We evaluate our models with data gathered from two user studies. We show that providing just a single correct word of context dramatically improves alignment success from 65% to 84%. We find that a majority of users provide such context without being explicitly instructed to do so. We find that the revision model is superior when users modify words in their initial recognition, improving alignment success from 73% to 83%. We show how our models can easily incorporate prior information about correction location and we show that such information aids alignment success. Last, we observe that users speak their intended text faster and with fewer re-recordings than if they are forced to speak misrecognized text.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129630941","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373266
F. Flego, M. Gales
Adaptive training is a powerful approach for building speech recognition systems on non-homogeneous training data. Recently approaches based on predictive model-based compensation schemes, such as Joint Uncertainty Decoding (JUD) and Vector Taylor Series (VTS), have been proposed. This paper reviews these model-based compensation schemes and relates them to factor-analysis style systems. Forms of Maximum Likelihood (ML) adaptive training with these approaches are described, based on both second-order optimisation schemes and Expectation Maximisation (EM). However, discriminative training is used in many state-of-the-art speech recognition. Hence, this paper proposes discriminative adaptive training with predictive model-compensation approaches for noise robust speech recognition. This training approach is applied to both JUD and VTS compensation with minimum phone error training. A large scale multi-environment training configuration is used and the systems evaluated on a range of in-car collected data tasks.
{"title":"Discriminative adaptive training with VTS and JUD","authors":"F. Flego, M. Gales","doi":"10.1109/ASRU.2009.5373266","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373266","url":null,"abstract":"Adaptive training is a powerful approach for building speech recognition systems on non-homogeneous training data. Recently approaches based on predictive model-based compensation schemes, such as Joint Uncertainty Decoding (JUD) and Vector Taylor Series (VTS), have been proposed. This paper reviews these model-based compensation schemes and relates them to factor-analysis style systems. Forms of Maximum Likelihood (ML) adaptive training with these approaches are described, based on both second-order optimisation schemes and Expectation Maximisation (EM). However, discriminative training is used in many state-of-the-art speech recognition. Hence, this paper proposes discriminative adaptive training with predictive model-compensation approaches for noise robust speech recognition. This training approach is applied to both JUD and VTS compensation with minimum phone error training. A large scale multi-environment training configuration is used and the systems evaluated on a range of in-car collected data tasks.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117247743","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 : 2009-12-01DOI: 10.1109/ASRU.2009.5373287
Spiros Dimopoulos, E. Fosler-Lussier, Chin-Hui Lee, A. Potamianos
In this paper, we investigate a variety of spectral and time domain features for explicitly modeling phonetic transitions in speech recognition. Specifically, spectral and energy distance metrics, as well as, time derivatives of phonological descriptors and MFCCs are employed. The features are integrated in an extended Conditional Random Fields statistical modeling framework that supports general-purpose transition models. For evaluation purposes, we measure both phonetic recognition task accuracy and precision/recall of boundary detection. Results show that when transition features are used in a CRF-based recognition framework, recognition performance improves significantly due to the reduction of phone deletions. The boundary detection performance also improves mainly for transitions among silence, stop, and fricative phonetic classes.
{"title":"Transition features for CRF-based speech recognition and boundary detection","authors":"Spiros Dimopoulos, E. Fosler-Lussier, Chin-Hui Lee, A. Potamianos","doi":"10.1109/ASRU.2009.5373287","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373287","url":null,"abstract":"In this paper, we investigate a variety of spectral and time domain features for explicitly modeling phonetic transitions in speech recognition. Specifically, spectral and energy distance metrics, as well as, time derivatives of phonological descriptors and MFCCs are employed. The features are integrated in an extended Conditional Random Fields statistical modeling framework that supports general-purpose transition models. For evaluation purposes, we measure both phonetic recognition task accuracy and precision/recall of boundary detection. Results show that when transition features are used in a CRF-based recognition framework, recognition performance improves significantly due to the reduction of phone deletions. The boundary detection performance also improves mainly for transitions among silence, stop, and fricative phonetic classes.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123563996","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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