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This paper introduces the use of two new features for speaker identification, Residual Phase Cepstrum Coefficients (RPCC) and Glottal Flow Cepstrum Coefficients (GLFCC), to capture speaker-specific characteristics from their vocal excitation patterns. Results on a cross-lingual speaker identification task taken from the NIST 2004 SRE demonstrate that these RPCC and GLFCC features are significantly...
For GMM-UBM based text-independent speaker recognition, the performance decreases significantly when the utterance is getting too short, and that is mostly due to the lack of distinguishable information from a single kind of feature. Fusion of different features followed by a dimensionality reduction process has been proved useful to provide a satisfying solution. However, some fusion methods based...
Parametric histogram equalization (PHEQ) is a simple and effective way to reduce noise and channel effects in automatic speech recognition. Recently, we proposed to adapt the PHEQ transform towards individual test utterance to further improve the performance of PHEQ. In this paper, we improve the adaptation of PHEQ by exploiting the correlation between speech feature elements. Previously, PHEQ is...
Cough Recognition is a valuable classification problem in healthcare. Generally, feature representation contributes a lot to the overall classifying performance. In this paper, a novel feature extraction method, Gammatone Cepstral Coefficients (GTCC), is investigated for cough recognition. The accuracy of GTCC comparing with MFCC is evaluated on a designed cough dataset following a 10 fold cross-validation...
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