Name
Abstract | ||
|---|---|---|
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/Nonspeech 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). |
Year | Venue | Field |
|---|---|---|
2010 | European Signal Processing Conference | Speech processing,Mel-frequency cepstrum,Speech coding,Pattern recognition,Voice activity detection,Computer science,Speech recognition,Speaker recognition,Artificial intelligence,Codec2,Linear predictive coding,Acoustic model |
DocType | ISSN | Citations |
Conference | 2219-5491 | 4 |
PageRank | References | Authors |
0.48 | 9 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Unal Zubari | 1 | 25 | 1.94 |
| ezgi can ozan | 2 | 18 | 4.44 |
| Banu Oskay Acar | 3 | 27 | 3.01 |
| Tolga Ciloglu | 4 | 34 | 2.87 |
| Ersin Esen | 5 | 92 | 14.15 |
| Tugrul K. Ates | 6 | 35 | 3.70 |
| Duygu Oskay Önür | 7 | 6 | 0.87 |