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On the Use of Nearest Feature Line for Speaker Identification
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 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 measures. We have applied the improved
NFL to speaker identification 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 strtegy 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.
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