Short- and long-term speech features for hybrid HMM-i-vector based speaker diarization system
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Cita com:
hdl:2117/101681
Tipus de documentComunicació de congrés
Data publicació2016
Condicions d'accésAccés obert
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Abstract
i-vectors have been successfully applied over the last years in speaker recognition tasks. This work aims at assessing the suitability of i-vector modeling within the frame of speaker diarization task. In such context, a weighted cosine-distance between two different sets of i-vectors is proposed for speaker clustering. Speech clusters generated by Viterbi segmentation are first modeled by two different i-vectors. Whilst the first i-vector represents the distribution of the commonly used short-term Mel Frequency Cepstral Coefficients, the second one depicts a selection of voice quality and prosodic features. In order to combine both short- and long-term speech statistics, the cosine-distance scores of those two i-vectors are linearly weighted to obtain a unique similarity score. The final fused score is then used as speaker clustering distance. Our experimental results on two different evaluation sets of the Augmented Multi-party Interaction corpus show the suitability of combining both sources of information within the i-vector space. Our experimental results show that the use of i-vector based clustering technique provide a significant improvement, in terms of diarization error rate, than those based on Gaussian Mixture Modeling technique. Furthermore, this work also reports a significant speaker error reduction by augmenting short-term based i-vector clustering with a second i-vector estimated from voice quality and prosody related speech features.
CitacióZewoudie, A., Jordi Luque, Hernando, J. Short- and long-term speech features for hybrid HMM-i-vector based speaker diarization system. A: The Speaker and Language Recognition Workshop. "ODYSSEY 2016 - The Speaker and Language Recognition Workshop". Bilbao: 2016, p. 400-406.
Versió de l'editorhttp://www.isca-speech.org/archive/Odyssey_2016/pdfs/18.pdf
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Odyssey Abraham.pdf | Paper | 353,2Kb | Visualitza/Obre |