W niniejszej rozprawie zaprezentowano wyniki badań mających na celu zdefiniowanie nowych sposobów segmentacji i parametryzacji sygnału mowy polskiej. Prowadzone prace miały charakter zarówno teoretyczny (np. kryteria ewaluacji, funkcje kosztu, rozwaŜania na temat WPCT i optymalizacji bazy) jak i praktyczny (implementacje algorytmów, ewaluacja skuteczności na podstawie bazy mowy Corpora).
Wyniki otrzymane za pomocą opracowanych algorytmów, przedstawione w rozdziałach 4 oraz 5 dowodzą słuszności następujących tez rozprawy:
1. Transformacja falkowa jest odpowiednim narzędziem do analizy sygnałów mowy.
2. Zastosowanie transformacji falkowej umoŜliwia racjonalną, nierównomierną segmentację sygnału mowy polskiej.
3. Transformacja falkowa umoŜliwia efektywną ekstrakcję parametrów sygnału w systemach rozpoznawania mowy polskiej.
Wszystkie cele pracy wyszczególnione w rozdziale 1.1, zostały zrealizowane. W tym celu konieczne było opracowanie nowatorskich metod i algorytmów.
Wkładem autora są:
1. Dwie nowe metody falkowej, nierównomiernej segmentacji sygnału mowy bez znajomości transkrypcji. Na szczególną uwagę zasługuje algorytm wyznaczania mapy istotności pasm falkowych i generowania dyskretnej funkcji zdarzeń, wykorzystanej do segmentacji sygnału (Rozdz. 4.4).
2. Nowe sposoby skutecznej parametryzacji falkowej dla systemów rozpoznawania mowy (Rozdz. 5.3 i 5.4).
3. Systematyzacja i uporządkowanie kryteriów oceny segmentacji i parametryzacji sygnału mowy (Rozdz. 4.3 i 5.1).
4. Przedstawienie sposobu dokładnej aproksymacji skal psychoakustycznych przez paczkową transformację falkową, na przykładzie skali melowej (Rozdz. 5.4.1).
5. Definicja optymalnej bazy dekompozycji zbioru zróŜnicowanych sygnałów i algorytm Mean Best Basis - uogólnienie algorytmu BB, do wyznaczania tej bazy (Rozdz. 5.4.2).
6. Zastosowanie WPCT - paczkowej transformacji falkowo-kosinusowej do wyznaczenia nowych schematów dekompozycji i parametryzacji sygnału mowy metodą MBB (Rozdz. 5.4.2).
7. Nowa funkcja kosztu – wskaźnik koncentracji, nadająca się do zastosowania w algorytmach BB oraz MBB, i zapewniająca skuteczne generowanie schematów dekompozycji w oparciu o transformację WPCT (Rozdz. 5.4.2).
Zakończenie
Oprócz wyszczególnionych idei i rozwiązań, wynikiem prac są takŜe działające implementacje wszystkich omawianych algorytmów. Powstałe oprogramowanie moŜe słuŜyć do dalszych badań nad problematyką falkowego przetwarzania mowy oraz być istotnym fragmentem prototypu automatycznego systemu rozpoznawania mowy.
Przedstawione rezultaty były publikowane i prezentowane na konferencjach międzynarodowych.
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