Speech Unit Category based Short Utterance Speaker Recognition

Nakhat Fatima1, Xiaojun Wu1 and Thomas Fang Zheng1

  1. Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University
    100084, Beijing, China
    fatima@cslt.riit.tsinghua.edu.cn, xjwu@tsinghua.edu.cn, fzheng@tsinghua.edu.cn


Information of speech units like vowels, consonants and syllables can be a kind of knowledge used in text-independent Short Utterance Speaker Recognition (SUSR) in a similar way as in text-dependent speaker recognition. In such tasks, data for each speech unit, especially at the time of recognition, is often not enough. Hence, it is not practical to use the full set of speech units because some of the units might not be well trained. To solve this problem, a method of using speech unit categories rather than individual phones is proposed for SUSR, wherein similar speech units are put together, hence solving the problem of sparse data. We define Vowel, Consonant, and Syllable Categories (VC, CC and SC) with Standard Chinese (Putonghua) as a reference. A speech utterance is recognized into VC, CC ad SC sequences which are used to train Universal Background Models (UBM) for each speech unit category in the training procedure, and to perform speech unit category dependent speaker recognition, respectively. Experimental results in Gaussian Mixture Model-Universal Background Model (GMM-UBM) based system give a relative equal error rate (EER) reduction of 54.50% and 40.95% from minimum EERs of VCs and SCs, respectively, for 2 seconds of test utterance compared with the existing SUSR systems.

Key words

Short Utterance Speaker Recognition, Vowel Categories, Universal Background Vowel Category Model

Digital Object Identifier (DOI)


Publication information

Volume 9, Issue 4 (December 2012)
Special Issue on Recent Advances in Systems and Informatics
Year of Publication: 2012
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

DownloadAvailable in PDF
Portable Document Format

How to cite

Fatima, N., Wu, X., Zheng, T. F.: Speech Unit Category based Short Utterance Speaker Recognition. Computer Science and Information Systems, Vol. 9, No. 4, 1407-1430. (2012)