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Tytuł:: Application of automatic speech recognition to medical reports spoken in Polish
Autorzy:: Hnatkowska, B.
Sas, J.
Powiązania:: https://bibliotekanauki.pl/articles/333379.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: systemy informacji medycznej
modele językowe
automatic speech recognition
hospital information systems
language models
Opis:: 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.
Źródło:: Journal of Medical Informatics & Technologies; 2008, 12; 223-229
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Optimal spoken dialog control in hands-free medical information systems
Autorzy:: Sas, J.
Powiązania:: https://bibliotekanauki.pl/articles/333081.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: rozpoznawanie mowy automatyczne
optymalizacja genetyczna
systemy informacji medycznej
automatic speech recognition
genetic optimization
medical information systems
Opis:: In the paper a method of optimal selection of utterances used as command entry-words for voice controlled application is presented. Voice controlled programs seem to be particularly useful in the area of medical informatics, where a physician interacts with a program by voice while operating the medical device or being involved in examinations requiring manual activities. The proposed method selects command words from sets of proposals defined for each command so as to minimize the overall probability of incorrect command recognition. First the entry-word dissimilarity matrix is calculated. The word dissimilarities are evaluated using HMM models consisting of appropriately trained acoustic models of the phonemes constituting words. The trained HMM is used as the sample utterance generator for the word. The artificially created utterance samples are then recognized by speech recognizers created for pairs of words. The estimation of correct recognition probability is used as the word dissimilarity measure. The word dissimilarities are then used to determine the average assessment of words selections that can be used as commands. Selection is created by choosing single word from sets of candidates defined for each command. Finally, suboptimal selection is found by using genetic algorithm. Experiments carried out prove that suboptimal selection of command entry-words can observably increase the accuracy of spoken commands recognition in many cases.
Źródło:: Journal of Medical Informatics & Technologies; 2009, 13; 113-120
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Building compact language models for medical speech recognition in mobile devices with limited amount of memory
Autorzy:: Sas, J.
Powiązania:: https://bibliotekanauki.pl/articles/332971.pdf
Data publikacji:: 2012
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: automatyczne rozpoznawanie mowy
medyczne systemy informacyjne
modelowanie języka
automatic speech recognition
medical information systems
language modeling
Opis:: The article presents the method of building compact language model for speech recognition in devices with limited amount of memory. Most popularly used bigram word-based language models allow for highly accurate speech recognition but need large amount of memory to store, mainly due to the big number of word bigrams. The method proposed here ranks bigrams according to their importance in speech recognition and replaces explicit estimation of less important bigrams probabilities by probabilities derived from the class-based model. The class-based model is created by assigning words appearing in the corpus to classes corresponding to syntactic properties of words. The classes represent various combinations of part of speech inflectional features like number, case, tense, person etc. In order to maximally reduce the amount of memory necessary to store class-based model, a method that reduces the number of part-of-speech classes has been applied, that merges the classes appearing in stochastically similar contexts in the corpus. The experiments carried out with selected domains of medical speech show that the method allows for 75% reduction of model size without significant loss of speech recognition accuracy.
Źródło:: Journal of Medical Informatics & Technologies; 2012, 20; 111-119
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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