8 research outputs found
The acoustic diversity in the phoneme inventories of the world鈥檚 languages
A comparative analysis of multi-language speech samples is conducted using acoustic characteristics of phoneme realisations in spoken languages. Different approaches to investigation of phonemic diversity in the context of language evolution are compared and discussed. We introduced our approach (materials and methods) and presented preliminary results of research. We built an online database dedicated to voice acquisition and a storage of good quality speech samples collected across the globe. Software designed for automatic extraction and analysis of phonemes was developed and adapted for languages classification. Research involves both experimental and theoretical works that aim at gaining knowledge about phonetic diversity of languages across the world. Additionally, the expected results may be applied to verify the hypothesis of modern languages expansion from Africa, brought to attention by Atkinson
System analizy i zarz膮dzania danymi na potrzeby przetwarzania sygna艂贸w
This paper presents framework for managing analysis of scientific data. The framework was build on sole purpose of research on signal processing and speech technology but can be successfully adapted to other scientific problems.Artyku艂 przedstawia 艣rodowisko zarz膮dzania analizami danych naukowych. System zosta艂 stworzony na potrzeby bada艅 nad przetwarzaniem sygna艂贸w i technologi膮 mowy, ale mo偶e by膰 z powodzeniem zastosowany w innych problemach naukowych
Baza danych nagra艅 mowy dla analizy por贸wnawczej r贸偶noj臋zycznych fonem贸w
The paper presents a system of collecting and analyzing multi-language speech samples for research on characteristics of phonemes in several hundred world languages. We describe the implementation: database and webpage. The content and form of the database and applications for development of the new methods of speech analysis are presented.Artyku艂 prezentuje system gromadzenia, archiwizacji i akustycznej analizy wieloj臋zycznych pr贸bek mowy. G艂贸wnym celem bada艅 jest analiza por贸wnawcza fonem贸w dla kilkuset j臋zyk贸w i stworzenie drzewa genealogicznego j臋zyk贸w 艣wiata. Opisana zosta艂a implementacja systemu, jako bazy danych z portalem internetowym. Przedstawiono informacje dotycz膮ce zawarto艣ci i formy bazy, perspektyw rozwoju i zastosowa艅 w lingwistyce komputerowej
Rejestracja multimodalnego korpusu danych dla automatycznego przetwarzania j臋zyka migowego
This paper presents the creation of a Polish Sign Language corpus suitable for recognition research and automatic translation of sign language. The recording approach used and the captured data modalities are presented, as well as the description of the acquisition system implementation. The evaluation of the collected corpus is presented and compared to other available resources.Artyku艂 prezentuje utworzenie korpusu nagra艅 gest贸w Polskiego J臋zyka Migowego na potrzeby bada艅 mo偶liwo艣ci rozpoznawania i automatycznego t艂umaczenia j臋zyka migowego. Zaprezentowano podej艣cie i metodyk臋 tworzenia bazy nagra艅 oraz opisano implementacj臋 systemu akwizycji. Przedstawiono tak偶e ewaluacj臋 zebranych danych pod k膮tem rozpoznawania gest贸w j臋zyka migowego oraz por贸wnano z innymi, dost臋pnymi zasobami
Baza danych s艂ownika j臋zyka polskiego ze statystykami s艂贸w dla systemu automatycznego rozpoznawania mowy
A dictionary of Polish implemented as a data base for automatic speech recognition is presented. The dictionary allows improvement of recognition by language modelling using statistics stored in the data base. The data currently kept in the database are presented as well.Artyku艂 opisuje s艂ownik j臋zyka polskiego zaimplementowany w postaci bazy danych na potrzeby systemu rozpoznawania mowy. Przedstawiono zastosowania s艂ownika do poprawienia jako艣ci rozpoznania przez modelowanie j臋zyka z wykorzystaniem danych przechowywanych w bazie. Zawarto tak偶e informacje na temat danych znajduj膮cych si臋 w bazie na koniec stycznia 2011 roku
Comparative study of SQLite and Berkeley DB implementations of n-gram model of Polish language
Por贸wnawcze studium implementacji modelu n gramowego j臋zyka polskiego w SQLite i Berkeley DB
Aspects of applying databases in computational linguistics are presented. An example of a dictionary and an n-gram model of the AGH automatic speech recognition system is depicted as well. An advantage of Berkeley DB, comparing to SQLite in time efficiency aspect is shown on this case.Przedstawiono zagadnienia dotycz膮ce stosowania baz danych w lingwistyce komputerowej. Om贸wiono tak偶e przyk艂ad s艂ownika i modelu n-gramowego systemu rozpoznawania mowy AGH. Pokazano na tym przyk艂adzie znacz膮c膮 przewag臋 implementacji wykonanej w Berkeley DB nad implementacj膮 SQLite w sensie wydajno艣ci czasowej
The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy
Background: Cardiovascular disease remains the leading cause of death in the European Union and worldwide. Constant improvement in cardiac care is leading to an increased number of patients with heart failure, which is a challenging condition in terms of clinical management. Cardiac resynchronization therapy is becoming more popular because of its grounded position in guidelines and clinical practice. However, some patients do not respond to treatment as expected. One way of assessing cardiac resynchronization therapy is with ECG analysis. Artificial intelligence is increasing in terms of everyday usability due to the possibility of everyday workflow improvement and, as a result, shortens the time required for diagnosis. A special area of artificial intelligence is machine learning. AI algorithms learn on their own based on implemented data. The aim of this study was to evaluate using artificial intelligence algorithms for detecting inadequate resynchronization therapy. Methods: A total of 1241 ECG tracings were collected from 547 cardiac department patients. All ECG signals were analyzed by three independent cardiologists. Every signal event (QRS-complex) and rhythm was manually classified by the medical team and fully reviewed by additional cardiologists. The results were divided into two parts: 80% of the results were used to train the algorithm, and 20% were used for the test (Cardiomatics, Cracow, Poland). Results: The required level of detection sensitivity of effective cardiac resynchronization therapy stimulation was achieved: 99.2% with a precision of 92.4%. Conclusions: Artificial intelligence algorithms can be a useful tool in assessing the effectiveness of resynchronization therapy