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A probabilistic model of Ancient Egyptian writing

Nederhof M.-J., Rahman F.

Journal of Language Modelling

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2017

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Vol. 5, No. 1

131--163

EN

This article offers a formalization of how signs form words in Ancient Egyptian writing, for either hieroglyphic or hieratic texts. The formalization is in terms of a sequence of sign functions, which concurrently produce a sequence of signs and a sequence of phonemes. By involving a class of probabilistic automata, we can define the most likely sequence of sign functions that relates a given sequence of signs to a given sequence of phonemes. Experiments with two texts are discussed.

2

Optimal acoustic model complexity selection in polish medical speech recognition

Sas J., Poreba T.

Journal of Medical Informatics & Technologies

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2011

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Vol. 17

115--122

EN

In the paper, the method of acoustic model complexity level selection for automatic speech recognition is proposed. Selection of the appropriate model complexity affects significantly the accuracy of speech recognition. For this reason the selection of the appropriate complexity level is crucial for practical speech recognition applications, where end user effort related to the implementation of speech recognition system is important. We investigated the correlation between speech recognition accuracy and two popular information criteria used in statistical model evaluation: Bayesian Information Criterion and Akaike Information Criterion computed for applied acoustic models. Experiments carried out for language models related to general medicine texts and radiology diagnostic reporting in CT and MR showed strong correlation of speech recognition accuracy and BIC criterion. Using this dependency, the procedure of Gaussian mixture count selection for acoustic model was proposed. Application of this procedure makes it possible to create the acoustic model maximizing the speech recognition accuracy without additional computational costs related to alternative cross-validation approach and without reduction of training set size, which is unavoidable in the case of cross-validation approach.

3

Application of local bidirectional language model to error correction in polish medical speech recognition

Sas J.

Journal of Medical Informatics & Technologies

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2010

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Vol. 15

127--134

EN

In the paper, the method of short word deletion errors correction in automatic speech recognition is described. Short word deletion errors appear to be a frequent error type in Polish speech recognition. The proposed speech recognition process consists of two stages. At the first stage the utterance is recognized by a typical speech recognizer based on forward bigram language model. At the second stage the word sequence recognized by the first stage recognizer is analyzed and such pairs of adjacent words in the recognized sequence are localized, which are likely to be separated by a short word like conjunction or preposition. The probability of short word appearance in context of found words is evaluated using centered trigrams and backward bigram language model for short words prone to deletion. The set of probabilistic language properties used to correct deletions is called here Local Bidirectional Language Model (in contrast to purely forward or backward model used typically in speech recognition). The decision of short word insertion is based on comparison of deletion error probability of the first stage recognizer and the error probability of the decision based only on centered trigrams and backward model. Despite its simplicity, the method proved to be effective in correcting deletion errors of most frequently appearing Polish prepositions. The method was tested in application to medical spoken reports recognition, where the overall short word deletion error rate was reduced by almost 45%.

4

Application of automatic speech recognition to medical reports spoken in Polish

Hnatkowska B., Sas J.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

223--229

EN

The paper presents an attempt to automatic speech recognition of Polish spoken medical texts. The attempt resulted in experimental system that can be used as a tool for practical applications. The system uses a typical recognition method based on Hidden Markov Model and domain-specific language model. Implemented software made it possible to conduct many experiments aimed on evaluation of the assumed approach usefulness. Obtained experiment results are presented and analyzed. The system architecture and the way in which it can be integrated with hospital information systems is also exposed.