TY - JOUR
T1 - On the use of nearest feature line for speaker identification
AU - Chen, Ke
AU - Wu, T
AU - Zhang, H
PY - 2002/12/1
Y1 - 2002/12/1
N2 - 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. (C) 2002 Elsevier Science B.V. All rights reserved.
AB - 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. (C) 2002 Elsevier Science B.V. All rights reserved.
UR - http://www.scopus.com/inward/record.url?scp=0036885175&partnerID=8YFLogxK
U2 - 10.1016/S0167-8655(02)00147-2
DO - 10.1016/S0167-8655(02)00147-2
M3 - Article
VL - 23
SP - 1735
EP - 1746
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 14
ER -