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https://idr.l4.nitk.ac.in/jspui/handle/123456789/8940Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ramteke, P.B. | |
| dc.contributor.author | Koolagudi, S.G. | |
| dc.contributor.author | Afroz, F. | |
| dc.date.accessioned | 2020-03-30T10:23:05Z | - |
| dc.date.available | 2020-03-30T10:23:05Z | - |
| dc.date.issued | 2016 | |
| dc.identifier.citation | Smart Innovation, Systems and Technologies, 2016, Vol.43, , pp.611-617 | en_US |
| dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/8940 | - |
| dc.description.abstract | This paper mainly focuses on detection of repetitions in stuttered speech. The stuttered speech signal is divided into isolated units based on energy. Mel-frequency cepstrum coefficients (MFCCs), formants and shimmer are used as features for repetition recognition. These features are extracted from each isolated unit. Using Dynamic Time Warping (DTW) the features of each isolated unit are compared with those subsequent units within one second interval of speech. Based on the analysis of scores obtained from DTW a threshold is set, if the score is below the set threshold then the units are identified as repeated events. Twenty seven seconds of speech data used in this work, consists of 50 repetition events. The result shows that the combination of MFCCs, formants and shimmer can be used for the recognition of repetitions in stuttered speech. Out of 50 repetitions, 47 are correctly identified. � Springer India 2016. | en_US |
| dc.title | Repetition detection in stuttered speech | en_US |
| dc.type | Book chapter | en_US |
| Appears in Collections: | 2. Conference Papers | |
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