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Erschienen in:
2017 | OriginalPaper | Buchkapitel
An Adaptive i-Vector Extraction for Speaker Verification with Short Utterance
verfasst von : Arnab Poddar, Md Sahidullah, Goutam Saha
Erschienen in: Pattern Recognition and Machine Intelligence
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Abstract
A prime challenge in automatic speaker verification (ASV) is to improve performance with short speech segments. The variability and uncertainty of intermediate model parameters associated with state-of-the-art i-vector based ASV system, extensively increases in short duration. To compensate increased variability, we propose an adaptive approach for estimation of model parameters. The pre-estimated universal background model (UBM) parameters are used for adaptation. The speaker models i.e., i-vectors are generated with the proposed adapted parameters. The ASV performance with the proposed approach considerably outperformed conventional i-vector based system on publicly available speech corpora, NIST SRE 2010, especially in short duration, as required in real-world applications.