A training algorithm for statistical sequence recognition with applications to transition-based speech recognition

Bourlard, Hervé; Konig, Yochai; Morgan, Nelson

doi:10.1109/97.508165

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…However, useful information such as linguistic constraints and longer acoustic features should be added to improve CM performance. In this section, we firstly imposed dependence between phones as explained in Figure.2, then compute the enhanced posteriors via Recursive Estimation and Maximization of A Posteriori Probabilities (REMAP) proposed in [8], and finally propose two CMs based on this enhanced posteriors.…”

Section: Methodsmentioning

confidence: 99%

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MLP-based Global Posterior Confidence Measure for Spoken Term Detection

Xiao

Gao

Zhang

et al. 2011

2011 International Conference on Computational and Information Sciences

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Confidence measures (CM) plays an important role in spoken term detection (STD) systems. Traditionally, confidence measures based on multi-layer perceptron (MLP) is computed via accumulating the frame-level phone posterior probabilities, where only short acoustic context information is used and some useful information from linguistic constraints is lost. In this paper, we propose two approaches to calculate the MLP-based CMs which can integrate language prior as well as sentence-level acoustic context. Experimental results show that the proposed approaches outperform the traditional one significantly. Moreover, fusing our proposed CM with the HMM/GMM based CM shows further performance improvement to our baseline system.

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Section: Methodsmentioning

confidence: 99%

“…Typically, the posterior probability can be estimated either by a generative model such as HMM/GMM [9] or a discriminative model such as multi-layer perceptron (MLP) [7], [8].…”

Section: Introductionmentioning

confidence: 99%

MLP-based Global Posterior Confidence Measure for Spoken Term Detection

Xiao

Gao

Zhang

et al. 2011

2011 International Conference on Computational and Information Sciences

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“…In this paper: (1) we demonstrate that the original HMM/ANN system 3, 4] trained using local criteria indeed optimizes the global posterior probability, given certain well-de ned assumptions (2) we use the REMAP algorithm to derive a f o r w ard-backward training algorithm for the original HMM/ANN system (3) we demonstrate the performance of these algorithms on the task-independent Phonebook database.…”

Section: Introductionmentioning

confidence: 92%

Estimation of global posteriors and forward-backward training of hybrid HMM/ANN systems

Hennebert¹,

Ris²,

Bourlard³

et al. 1997

5th European Conference on Speech Communication and Technology (Eurospeech 1997)

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The results of our research presented in this paper is two-fold. First, an estimation of global posteriors is formalized in the framework of hybrid HMM/ANN systems. It is shown that hybrid HMM/ANN systems, in which the ANN part estimates local posteriors, can be used to modelize global model posteriors. This formalization provides us with a clear theory in which both REMAP and \classical" Viterbi trained hybrid systems are uni ed. Second, a new forward-backward training of hybrid HMM/ANN systems is derived from the previous formulation. Comparisons of performance between Viterbi and forward-backward hybrid systems are presented and discussed.

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“…However, using similar factorizations and assumptions as above, Bourlard, Konig and Morgan (1996) and Hennebert, Ris, Bourlard, Renals and Morgan (1997) demonstrated that a generalized EM algorithm exists for the optimization of the parameters of acceptor HMMs. The E-step consists of estimating the posterior state/time probabilities given the acoustic data; the M-step involves the parameter optimization of the local posterior probability estimators (typically artificial neural networks).…”

Section: Generative Hmms and Acceptor Hmmsmentioning

confidence: 98%

Confidence measures from local posterior probability estimates

Williams

Renals

1999

Computer Speech & Language

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In this paper we introduce a set of related confidence measures for large vocabulary continuous speech recognition (LVCSR) based on local phone posterior probability estimates output by an acceptor HMM acoustic model. In addition to their computational efficiency, these confidence measures are attractive as they may be applied at the state-, phone-, word-or utterance-levels, potentially enabling discrimination between different causes of low confidence recognizer output, such as unclear acoustics or mismatched pronunciation models. We have evaluated these confidence measures for utterance verification using a number of different metrics. Experiments reveal several trends in "profitability of rejection", as measured by the unconditional error rate of a hypothesis test. These trends suggest that crude pronunciation models can mask the relatively subtle reductions in confidence caused by out-of-vocabulary (OOV) words and disfluencies, but not the gross model mismatches elicited by non-speech sounds. The observation that a purely acoustic confidence measure can provide improved performance over a measure based upon both acoustic and language model information for data drawn from the Broadcast News corpus, but not for data drawn from the North American Business News corpus suggests that the quality of model fit offered by a trigram language model is reduced for Broadcast News data. We also argue that acoustic confidence measures may be used to inform the search for improved pronunciation models.

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A training algorithm for statistical sequence recognition with applications to transition-based speech recognition

Cited by 9 publications

References 4 publications

MLP-based Global Posterior Confidence Measure for Spoken Term Detection

MLP-based Global Posterior Confidence Measure for Spoken Term Detection

Estimation of global posteriors and forward-backward training of hybrid HMM/ANN systems

Confidence measures from local posterior probability estimates

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