• Journal of Digital Convergence
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• v.13 no.1
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• pp.243-248
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• 2015

Recognition model is not defined when you configure a model, Been added to the model after model building awareness, Model a model of the clustering due to lack of recognition models are generated by modeling is causes the degradation of the recognition rate. In order to improve decision tree state tying modeling using parameter estimation of Bayesian method. The parameter estimation method is proposed Bayesian method to navigate through the model from the results of the decision tree based on the tying state according to the maximum probability method to determine the recognition model. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method error rate reduction of 1.29% compared with baseline model, which is slightly better performance than the existing approach.

• MALSORI
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• no.45
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• pp.117-126
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• 2003

Decision tree-based state tying has been proposed in recent years as the most popular approach for clustering the states of context-dependent hidden Markov model-based speech recognition. The aims of state tying is to reduce the number of free parameters and predict state probability distributions of unseen models. But, when doing state tying, the size of a decision tree is very important for word independent recognition. In this paper, we try to construct optimized decision tree based on the average of feature vectors in state pool and the number of seen modes. We observed that the proposed optimal decision tree is effective in predicting the state probability distribution of unseen models.

• The Journal of the Acoustical Society of Korea
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• v.13 no.3
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• pp.63-70
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• 1994

In this Paper, we propose a new codeword tying algorithm based on a tree structured classfier. The proposed algorithm which can be viewed as a kind of soft decision using statistical properties between codewords and states has an advantage of fast construction, and guarantees a unique optimal solution. Also, it can easily be applied to any speech recognition system based on discrete hidden Markov model (HMM). Experimental results on speaker-independent isolated word recognition show error reduction of $6\%$ for the codebook of size 256 and $9\%$ for 512 size and also HMM parameter reduction of about $20\%$.

• Proceedings of the KSPS conference
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• 2002.11a
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• pp.197-200
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• 2002

In this paper, we propose a phoneme duration modeling in a speech recognition system based on disicion tree state tying. We assume that phone duration has a Gamma distribution. In a training mode, we model mean and variance of each state duration in context-independent phone model based on decision tree state tying. In a recognition mode, we get mean and variance of each context-dependent phone duration form state duration information obtaind during training mode. We make a comparative study of the proposed meth with conventinal methods. Our method results in good performance compared with conventional methods.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.339-344
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• 2012

Established continuous vocabulary recognition system improved recognition rate by using decision tree based tying modeling method. However, since system model cannot support the retrieve of phoneme data, it is hard to secure the accuracy. In order to improve this problem, we remodeled a system that could retrieve probabilistic model from continuous vocabulary clustering model to phoneme unit. Therefore in this paper showed 95.88%of recognition rate in system performance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.167-176
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• 2005

This paper describes how the state tying model based on the decision tree which is one of Acoustic models used for speech recognition optimizes the model by reducing the number of mixture Gaussians of the output probability distribution. The state tying modeling uses a finite set of questions which is possible to include the phonological knowledge and the likelihood based decision criteria. And the recognition rate can be improved by increasing the number of mixture Gaussians of the output probability distribution. In this paper, we'll reduce the number of mixture Gaussians at the highest point of recognition rate by clustering the Gaussians. Bhattacharyya and Euclidean method will be used for the distance measure needed when clustering. And after calculating the mean and variance between the pair of lowest distance, the new Gaussians are created. The parameters for the new Gaussians are derived from the parameters of the Gaussians from which it is born. Experiments have been performed using the STOCKNAME (1,680) databases. And the test results show that the proposed method using Bhattacharyya distance measure maintains their recognition rate at $97.2\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. And the method using Euclidean distance measure shows that it maintains the recognition rate at $96.9\%$ and reduces the ratio of the number of mixture Gaussians by $1.0\%$. Then the methods can optimize the state tying model.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.369-377
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• 2010

In vocabulary recognition systems based on HMM(Hidden Markov Model)s, training process unseen model bring on show a low recognition rate. If recognition vocabulary modify and make an addition then recreated modeling of executed database collected and training sequence on account of bring on additional expenses and take more time. This study suggest efficient context dependent process modeling method using decision tree-based state tying. On study suggest method is reduce recreated of model and it's offered that robustness and accuracy of context dependent acoustic modeling. Also reduce amount of model and offered training process unseen model as concerns context dependent a likely phoneme model has been used unseen model solve the matter. System performance as a result of represent vocabulary dependence recognition rate of 98.01%, vocabulary independence recognition rate of 97.38%.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.177-183
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• 2010

In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.

• Korean Journal of Cognitive Science
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• v.15 no.4
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• pp.1-14
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• 2004

When two objects are tying in the same visual direction there occurs abrupt depth change between two objects, which is against the assumption of the computational model for stereopsis on the surfaces in a natural scene. For this reason, this stimulus configuration is popularly used in the studies for the effectiveness of the constraints employed in the computational model. Contrary to the results from two nails (or objects) tying in the same visual direction, the two different surfaces from random-dot stereogram (RDS) in the same situation can be seen simultaneously in the different depth. The seemingly contradictory results between two situations my reflect the different strategies imposed by binocular mechanism for each situation during binocular matching process. Otherwise, the surfaces tying in the same visual direction is not equivalent situation to two objects tying in the same visual direction with regards to matching process. In order to examine above possibilities, the stereoscopic depth of the surface was measured after manipulating the visual direction of the surface elements. The visual direction of each dot pair from different surfaces in RDS (in Experiment 1) or the visual direction of line (hawing rectangle with regard to that of the vertical line (in Experiment 2) was manipulated. The stereoscopic depth of the surface was found to be varied depending on visual direction of the surface elements in both RDS and line hawing stimulus. Similar to the results from two nails situation depth of the surface was greatly reduced when each surface element was tying in the same visual direction as that of the other surface element or the other object. These results suggest that binocular mechanism imposes no different strategy in resolving correspondence problem in both two objects and two surfaces situation. And the results were discussed in the context of usefulness of the constraints employed in the computational model for stereopsis.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.395-398
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• 1994

In this paper the word recognition using sub-model based Hidden Markov Model was studied. Phoneme models were composed of 61 phonemes in therms of Korean language pronunciation characteristic. Using this, word model was maded by serial concatenation. But, in case of this phoneme concatenation, the second and the third phoneme of syllable are overlapped in distribution at the same time. So considering this, the method that combines the second and the third phoneme to one model was proposed. And to prevent the increase in number of model, similar phonemes were combined to one, and finially, 57 models were created. In experiment proper model structure of sub-model was searched for, and recognition results were compared. So similar recognition results were maded, and overall recognition rates were increased in case of using parameter tying method.