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Yayın Extraction and comparison of various prosodic feature sets on sentence segmentation task for Turkish broadcast news data(IEEE, 2014) Dalva, Doğan; Revidi, İzel D.; Güz, Ümit; Gürkan, HakanIn this work, prosodic features of the Turkish Broadcast News (BN) data are extracted using an open source prosodic feature extraction tool based on Praat. The profiles and effectiveness of these features are also investigated for the sentence segmentation task on the Turkish BN data. We not only used some combinations of the feature sets but also collected some of them in one prosodic feature model in order to achieve one of the best performance. The results of the experiments show that some combinations of the prosodic feature sets are very useful for the automatic sentence segmentation task on the Turkish BN data.Yayın Türkçe haber yayını verileri için bürünsel bilginin çıkarılması ve cümle bölütlemede kullanılması(IEEE, 2014-04-23) Dalva, Doğan; Revidi, İzel D.; Güz, Ümit; Gürkan, HakanBu çalışmada, Türkçe haber yayını verilerine ilişkin bürünsel özelliklerin açık kaynak kodlu yazılımlar ile çıkarılması ve bürünsel özellik gruplarının Otomatik Konuşma Tanıma (Automatic Speech Recognition) Sistemi çıkışından elde edilen metin üzerinde cümle bölütlemedeki başarımlarının karşılaştırılması gerçekleştirilmiştir.Özellikle cümle bölütleme işlevi için oldukça yüksek başarım oranına sahip bir bürünsel özellik seti elde edilmiştir.Yayın Prosodic, morphological and lexical feature extraction of Turkish broadcast news data(Işık Üniversitesi, 2014-06-05) Revidi, İzel D.; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Yüksek Lisans ProgramıSentence segmentation from speech is part of a process that aims at enriching the unstructured stream of words that are the output of standard speech recognizers. Its role is to find the sentence units in this stream of words. Sentence segmentation is a preliminary step toward speech understanding. Once the sentence boundaries are detected, further syntactic and/or semantic analysis can be performed on these sentences. Usually, speech recognizer output lacks the textual cues to these entities (such as headers, paragraphs, sentence punctuation, and capitalization). However, speech provides extra non-lexical cues, related to features like pitch, energy, pause and word durations as prosodic features; verb, noun or adjective as a morphological features and also lexical features. These prosodic, morphological and lexical features are provides a complementary information for segmentation of speech into sentences. Our goal is examine feature the extraction and use of prosodic information which has been done in previous works, in addition to lexical features and morphological for spoken language processing of Turkish with open source tools.
