1 Ocak 2010 Konferans bildirisi Açık Erişim

Zubari, Unal; Ozan, Ezgi Can; Acar, Banu Oskay; Ciloglu, Tolga; Esen, Ersin; Ates, Tugrul K.; Onur, Duygu Oskay

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    <subfield code="a">Speech boundary detection contributes to performance of speech based applications such as speech recognition and speaker recognition. Speech boundary detector implemented in this study works on broadcast audio as a pre-processor module of a keyword spotter. Speech boundary detection is handled in 3 steps. At first step, audio data is segmented into homogeneous regions in an unsupervised manner. After an ACTIVITY/NON-ACTIVITY decision is made for each region, ACTIVITY regions are classified as Speech/Non-speech via Gaussian Mixture Model (GMM) based classification. GMM's are trained using a novel feature, Spectral Flow Direction (SFD), and an improved multi-band harmonicity feature in addition to widely used Mel Frequency Cepstral Coefficients (MFCC's).</subfield>
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