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.

Least squares glottal inverse filtering from the acoustic speech waveform

Abstract: Covariance analysis as a least squares approach for accurately performing glottal inverse filtering from the acoustic speech waveform is discussed. Best results are obtained by situating the analysis window within a stable closed glottis interval. Based on a linear model of speech production, it is shown that both the moment of glottal closure and opening can be determined from the normalized total squared error with proper choices of analysis window length and filter order. Results from actual speech are presented to illustrate the technique.

Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model

Abstract: This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm.

Experimental evaluation of features for robust speaker identification

Abstract: This correspondence presents an experimental evaluation of different features and channel compensation techniques for robust speaker identification. The goal is to keep all processing and classification steps constant and to vary only the features and compensations used to allow a controlled comparison. A general, maximum-likelihood classifier based on Gaussian mixture densities is used as the classifier, and experiments are conducted on the King speech database, a conversational, telephone-speech database. The features examined are mel-frequency and linear-frequency filterbank cepstral coefficients, linear prediction cepstral coefficients, and perceptual linear prediction (PLP) cepstral coefficients. The channel compensation techniques examined are cepstral mean removal, RASTA processing, and a quadratic trend removal technique. It is shown for this database that performance differences between the basic features is small, and the major gains are due to the channel compensation techniques. The best "across-the-divide" recognition accuracy of 92% is obtained for both high-order LPC features and band-limited filterbank features. >

Speech synthesis using concatenation of speech waveforms

Abstract: A high quality speech synthesizer in various embodiments concatenates speech waveforms referenced by a large speech database. Speech quality is further improved by speech unit selection and concatenation smoothing.

Multiple stage vector quantization for speech coding

Abstract: In this paper, we present a multiple stage vector quantization technique which allows easy expansion of the original vector quantizer design to operate at higher bit rates for lower distortion. The computation and storage reduction is achieved by the fact that the overall requirements are the sum of the requirements of each stage instead of an exponentially increasing function of the bit rate as in the original one stage design. In the case of Euclidean distance measures such as the log area ratio measure, experimental results show that the quantizer performance is very close to a theoretically predicted asymptotically optimal rate distortion relationship.