On the use of nearest feature line for speaker identification

Authors:
Ke Chen;Ting-Yao Wu;Hong-Jiang Zhang
Affiliations:
School of Computer Science, The University of Birmingham, Edgbaston, Birmingham B15 2TT, UK;National Laboratory of Machine Perception, The Center for Information Science, Peking University, Beijing 100871, China;Microsoft Research Asia, 5IF, Sigma Center, No. 49, Zhichun Road, Hai Dian District, Beijing 100080, China
Venue:
Pattern Recognition Letters
Year:
2002

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Abstract

As a new pattern classification method, nearest feature line (NFL) provides an effective way to tackle the sort of pattern recognition problems where only limited data are available for training. In this paper, we explore the use of NFL for speaker identification in terms of limited data and examine how the NFL performs in such a vexing problem of various mismatches between training and test. In order to speed up NFL in decision-making, we propose an alternative method for similarity measure. We have applied the improved NFL to speaker identification of different operating modes. Its text-dependent performance is better than the dynamic time warping (DTW) on the Ti46 corpus, while its computational load is much lower than that of DTW. Moreover, we propose an utterance partitioning strategy used in the NFL for better performance. For the text-independent mode, we employ the NFL to be a new similarity measure in vector quantization (VQ), which causes the VQ to perform better on the KING corpus. Some computational issues on the NFL are also discussed in this paper.