Abstract
A low-bit-rate linear predictive coder (LPC) that is based on variable-length segment quantization is presented. In this vocoder, the speech spectral-parameter sequence is represented as the concatenation of variable-length spectral segments generated by linearly time-warping fixed-length code segments. Both the sequence of code segments and the segment lengths are efficiently determined using a dynamic programming procedure. This procedure minimizes the spectral distance measure between the original and the coded spectral sequence in a given interval. An iterative algorithm is developed for designing fixed-length code segments for the training spectral sequence. It updates the segment boundaries of the training spectral sequence using an a priori codebook and updates the codebook using these segment sequences. The convergence of this algorithm is discussed theoretically and experimentally. In experiments, the performance of variable-length segment quantization for voice coding is compared to that of fixed-length segment quantization and vector quantization.
Original language | English |
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Pages (from-to) | 1437-1444 |
Number of pages | 8 |
Journal | IEEE Transactions on Acoustics, Speech, and Signal Processing |
Volume | 36 |
Issue number | 9 |
DOIs | |
Publication status | Published - 1988 Sep |
Externally published | Yes |
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ASJC Scopus subject areas
- Signal Processing
Cite this
LPC speech coding based on variable-length segment quantization. / Shiraki, Y.; Honda, Masaaki.
In: IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 36, No. 9, 09.1988, p. 1437-1444.Research output: Contribution to journal › Article
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TY - JOUR
T1 - LPC speech coding based on variable-length segment quantization
AU - Shiraki, Y.
AU - Honda, Masaaki
PY - 1988/9
Y1 - 1988/9
N2 - A low-bit-rate linear predictive coder (LPC) that is based on variable-length segment quantization is presented. In this vocoder, the speech spectral-parameter sequence is represented as the concatenation of variable-length spectral segments generated by linearly time-warping fixed-length code segments. Both the sequence of code segments and the segment lengths are efficiently determined using a dynamic programming procedure. This procedure minimizes the spectral distance measure between the original and the coded spectral sequence in a given interval. An iterative algorithm is developed for designing fixed-length code segments for the training spectral sequence. It updates the segment boundaries of the training spectral sequence using an a priori codebook and updates the codebook using these segment sequences. The convergence of this algorithm is discussed theoretically and experimentally. In experiments, the performance of variable-length segment quantization for voice coding is compared to that of fixed-length segment quantization and vector quantization.
AB - A low-bit-rate linear predictive coder (LPC) that is based on variable-length segment quantization is presented. In this vocoder, the speech spectral-parameter sequence is represented as the concatenation of variable-length spectral segments generated by linearly time-warping fixed-length code segments. Both the sequence of code segments and the segment lengths are efficiently determined using a dynamic programming procedure. This procedure minimizes the spectral distance measure between the original and the coded spectral sequence in a given interval. An iterative algorithm is developed for designing fixed-length code segments for the training spectral sequence. It updates the segment boundaries of the training spectral sequence using an a priori codebook and updates the codebook using these segment sequences. The convergence of this algorithm is discussed theoretically and experimentally. In experiments, the performance of variable-length segment quantization for voice coding is compared to that of fixed-length segment quantization and vector quantization.
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U2 - 10.1109/29.90372
DO - 10.1109/29.90372
M3 - Article
AN - SCOPUS:0024075701
VL - 36
SP - 1437
EP - 1444
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
SN - 1053-587X
IS - 9
ER -