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Topic Identification from Spoken TED-Talks
Vašš, Adam ; Ondel, Lucas Antoine Francois (oponent) ; Kesiraju, Santosh (vedoucí práce)
This thesis deals with the problems of language recognition and topic classification, using TED-LIUM corpus to train both the ASR and classification models. The ASR system is built using the Kaldi toolkit, achieving the WER of 16.6%. The classification problem is addressed using linear classification methods, specifically Multinomial Naive Bayes and Linear Support Vector Machines, the latter method achieving higher topic classification accuracy.
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Discovering Acoustic Units from Speech: a Bayesian Approach
Ondel, Lucas Antoine Francois ; Häb-Umbach, Reinhold (oponent) ; Glass, Jim (oponent) ; Burget, Lukáš (vedoucí práce)
From an early age, infants show an innate ability to infer linguistic structures from the speech signal long before they learn to read and write. In contrast, modern speech recognition systems require large collections of transcribed data to achieve a low error rate. The relatively recent field of Unsupervised Speech Learning has been dedicated to endow machines with a similar ability. As a part of this ongoing effort, this thesis focuses on the problem of discovering a set of acoustic units from a language given untranscribed audio recordings. Particularly, we explore the potential of Bayesian inference to address this problem. First, we revisit the state-of-the-art non-parametric Bayesian model for the task of acoustic unit discovery and derive a fast and efficient Variational Bayes inference algorithm. Our approach relies on the stick-breaking construction of the Dirichlet Process which allows expressing the model as a Hidden Markov Model-based phone-loop. With this model and a suitable mean-field approximation of the variational posterior, the inference is made with an efficient iterative algorithm similar to the Expectation-Maximization scheme. Experiments show that this approach performs a better clustering than the original model while being orders of magnitude faster. Secondly, we address the problem of defining a meaningful a priori distribution over the potential acoustic units. To do so, we introduce the Generalized Subspace Model, a theoretical framework that allows defining distributions over low-dimensional manifolds in high-dimensional parameter space. Using this tool, we learn a phonetic subspace - a continuum of phone embeddings-from several languages with transcribed recordings. Then, this phonetic subspace is used to constrain our system to discover acoustic units that are similar to phones from other languages. Experimental results show that this approach significantly improves the clustering quality as well as the segmentation accuracy of the acoustic unit discovery system. Finally, we enhance our acoustic units discovery model by using a Hierarchical Dirichlet Process prior instead of the simple Dirichlet Process. By doing so, we introduce a Bayesian bigram phonotactic language model to the acoustic unit discovery system. This approach captures more accurately the phonetic structure of the target language and consequently helps the clustering of the speech signal. Also, to fully exploit the benefits of the phonotactic language model, we derive a modified Variational Bayes algorithm that can balance the preponderance of the role of the acoustic and language model during inference.
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Topic Identification from Spoken TED-Talks
Vašš, Adam ; Ondel, Lucas Antoine Francois (oponent) ; Kesiraju, Santosh (vedoucí práce)
This thesis deals with the problems of language recognition and topic classification, using TED-LIUM corpus to train both the ASR and classification models. The ASR system is built using the Kaldi toolkit, achieving the WER of 16.6\%. The classification problem is addressed using linear classification methods, specifically Multinomial Naive Bayes and Linear Support Vector Machines, the latter method achieving higher topic classification accuracy.
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Discovering Acoustic Units from Speech: a Bayesian Approach
Ondel, Lucas Antoine Francois ; Häb-Umbach, Reinhold (oponent) ; Glass, Jim (oponent) ; Burget, Lukáš (vedoucí práce)
From an early age, infants show an innate ability to infer linguistic structures from the speech signal long before they learn to read and write. In contrast, modern speech recognition systems require large collections of transcribed data to achieve a low error rate. The relatively recent field of Unsupervised Speech Learning has been dedicated to endow machines with a similar ability. As a part of this ongoing effort, this thesis focuses on the problem of discovering a set of acoustic units from a language given untranscribed audio recordings. Particularly, we explore the potential of Bayesian inference to address this problem. First, we revisit the state-of-the-art non-parametric Bayesian model for the task of acoustic unit discovery and derive a fast and efficient Variational Bayes inference algorithm. Our approach relies on the stick-breaking construction of the Dirichlet Process which allows expressing the model as a Hidden Markov Model-based phone-loop. With this model and a suitable mean-field approximation of the variational posterior, the inference is made with an efficient iterative algorithm similar to the Expectation-Maximization scheme. Experiments show that this approach performs a better clustering than the original model while being orders of magnitude faster. Secondly, we address the problem of defining a meaningful a priori distribution over the potential acoustic units. To do so, we introduce the Generalized Subspace Model, a theoretical framework that allows defining distributions over low-dimensional manifolds in high-dimensional parameter space. Using this tool, we learn a phonetic subspace - a continuum of phone embeddings-from several languages with transcribed recordings. Then, this phonetic subspace is used to constrain our system to discover acoustic units that are similar to phones from other languages. Experimental results show that this approach significantly improves the clustering quality as well as the segmentation accuracy of the acoustic unit discovery system. Finally, we enhance our acoustic units discovery model by using a Hierarchical Dirichlet Process prior instead of the simple Dirichlet Process. By doing so, we introduce a Bayesian bigram phonotactic language model to the acoustic unit discovery system. This approach captures more accurately the phonetic structure of the target language and consequently helps the clustering of the speech signal. Also, to fully exploit the benefits of the phonotactic language model, we derive a modified Variational Bayes algorithm that can balance the preponderance of the role of the acoustic and language model during inference.
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Topic Identification from Spoken TED-Talks
Vašš, Adam ; Ondel, Lucas Antoine Francois (oponent) ; Kesiraju, Santosh (vedoucí práce)
This thesis deals with the problems of language recognition and topic classification, using TED-LIUM corpus to train both the ASR and classification models. The ASR system is built using the Kaldi toolkit, achieving the WER of 16.6%. The classification problem is addressed using linear classification methods, specifically Multinomial Naive Bayes and Linear Support Vector Machines, the latter method achieving higher topic classification accuracy.
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Topic Identification from Spoken TED-Talks
Vašš, Adam ; Ondel, Lucas Antoine Francois (oponent) ; Kesiraju, Santosh (vedoucí práce)
This thesis deals with the problems of language recognition and topic classification, using TED-LIUM corpus to train both the ASR and classification models. The ASR system is built using the Kaldi toolkit, achieving the WER of 16.6\%. The classification problem is addressed using linear classification methods, specifically Multinomial Naive Bayes and Linear Support Vector Machines, the latter method achieving higher topic classification accuracy.
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