Topic
Linear predictive coding
About: Linear predictive coding is a research topic. Over the lifetime, 6565 publications have been published within this topic receiving 142991 citations. The topic is also known as: Linear predictive coding, LPC.
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TL;DR: A speech encoding/decoding method calculates a short-term prediction error of an input speech signal that is divided on a time axis into blocks, represents the short- term prediction residue by a synthesized sine wave and a noise and encodes a frequency spectrum of each of the synthesised sineWave and noise to encode the speech signal.
Abstract: A speech encoding/decoding method calculates a short-term prediction error of an input speech signal that is divided on a time axis into blocks, represents the short-term prediction residue by a synthesized sine wave and a noise and encodes a frequency spectrum of each of the synthesized sine wave and the noise to encode the speech signal. The speech encoding/decoding method decodes the speech signal on a block basis and finds a short-term prediction residue waveform by sine wave synthesis and noise synthesis of the encoded speech signal. The speech encoding/decoding method then synthesizes the time-axis waveform signal based on the short-term prediction residue waveform of the encoded speech signal.
35 citations
TL;DR: In this article, the variation in the spectrum of an input signal per unit time is calculated over an analysis frame period, and when the frequency of spectrum variation falls in a predetermined range, the input signal of that frame is decided to be a speech signal.
Abstract: In method for detecting a speech period in a high-noise environment, the variation in the spectrum of an input signal per unit time is calculated over an analysis frame period, and when the frequency of spectrum variation falls in a predetermined range, the input signal of that frame is decided to be a speech signal.
35 citations
03 Apr 1990
TL;DR: Four varieties of line spectrum pair frequencies for speaker recognition applications and their corresponding performances are studied and show that each variety of LSP frequencies has a very high accurate rate.
Abstract: Four varieties of line spectrum pair (LSP) frequencies for speaker recognition applications and their corresponding performances are studied. The four varieties are all-LSP frequencies, odd or even line spectrum frequencies, the mean of adjacent LSP frequencies, and the difference of adjacent LSP frequencies. A speaker-based vector quantization approach is used in the evaluation. The results show that each variety of LSP frequencies has a very high accurate rate. >
35 citations
03 Apr 1990
TL;DR: The A* algorithm is proposed for finding the best quantization bit pattern of LSP frequency differences and achieves a better rate-distortion performance than the best results obtained in the previous study.
Abstract: A previously published study by the authors (Proc. ICASSP, p.394-7, 1988) of optimal quantization of line spectral pair (LSP) parameters is extended by incorporating delayed decisions coding (in frequency). The A* algorithm is proposed for finding the best quantization bit pattern of LSP frequency differences. The best coding pattern is obtained efficiently without an exhaustive, hence prohibitive, search. The proposed search achieves a better rate-distortion performance than the best results obtained in the previous study. At 30 bits/frame, a net gain of 2 bits/frame over the previous results, the novel method achieves 1-dB average spectral distortion. Most importantly, the number of frames with large spectral distortions (>2 dB), which can be subjectively disturbing and degrade the perceived quality of a speech coder, is significantly reduced. The search complexity of the A* algorithm is moderate. While the peak load is comparable to a nonoptimal M-algorithm, the average load is about an order of magnitude lower. >
35 citations
04 Aug 2002
TL;DR: An entropy based contrast function between the speech segments and the background noise is proposed, which exhibits better-behaved characteristics as compared to the energy-based methods.
Abstract: This paper addresses the issue of automatic word/sentence boundary detection in both quiet and noisy environments. We propose to use an entropy based contrast function between the speech segments and the background noise. A simplified data based scheme of computing the entropy of the speech data is presented. The entropy-based contrast exhibits better-behaved characteristics as compared to the energy-based methods. An adaptive threshold is used to determine the candidate speech segments, which are subjected to word/sentence constraints. Experimental. results show that this algorithm outperforms energy-based algorithms. The improved detection accuracy of speech segments results in at least 25% improvement of recognition performance for isolated speech and more than 16% for connected speech. For continuous speech, a preprocessing stage comprising of the proposed speech segment detection makes the overall HMM based scheme more computationally efficient by rejection of silence periods.
34 citations