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Yayın Automatic speech recognition system for Turkish spoken language(Işık Üniversitesi, 2012-06-21) Dalva, Doğan; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Yüksek Lisans ProgramıThe transmission and storage of speech sounds is possible for decades. In addition by using signal processing techniques, it is also possible tp process speech signals. By using time abd frequency analysis od speech signal and several machine learning algorithms, it is possible to build a system which is used to recognize spoken words. Such systems are called Automatic Speech Recognition systems. In our work, We have used the Automatic Speech Recognition system for Turkish spoken language which has built by BUSIM speech group. However, the output of the recognizer is the list of spoken words. Even for humans it is avery hard to understand a text without punctuation symbols. Hence to build more complex recognizer whose goal to perform topic segmentation and topic summarization, the output of ASR should be divided into sentences at first. Our goal is to build a system which performs the sentence segmentation. In our work We have used ASR system to obtain word level and phoneme level time marks and by using that time marks with the audio files, We have extracted prosodic features, where the prosodic properties of speech contains information about the punctuation in the text, which is not available at the output of ASR system.Yayın Co-training using prosodic, lexical and morphological information for automatic sentence segmentation of Turkish spoken language(Işık Üniversitesi, 2018-01-15) Dalva, Doğan; Güz, Ümit; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik Mühendisliği Doktora ProgramıSentence segmentation of speech aims detecting sentence boundaries in a stream of words output by the speech recognizer. Sentence segmentation is a preliminary step toward speech understanding. It is of particular importance for speech related applications, as most of the further processing steps; such as parsing, machine translation and information extraction, assume the presence of sentence boundaries. Typically, statistical methods require a huge amount of manually labeled data, which is time and labor consuming process to prepare. In this work, novel multiview semi-supervised learning strategies for the solution of sentence segmentation problem are proposed. The aim of this work is to and effective semi-supervised machine learning strategies when only a small set of sentence boundary labeled data is available. This work proposes three-view co-training and committee-based strategies incorporating with agreement, disagreement and self-combined strategies using lexical, morphological and prosodic information, and investigates performance of the proposed learning strategies against baseline, self-training and co-training. The experimental results show that the proposed learning strategies highly improve the sentence segmentation problem, since data sets can be represented by three redundantly suffcient and disjoint feature sets.
