Code-excited linear prediction

About: Code-excited linear prediction is a research topic. Over the lifetime, 2025 publications have been published within this topic receiving 28633 citations. The topic is also known as: CELP.

Low-band extension of CELP speech coder by harmonics recovery

Abstract: Most of the telephone speech being transmitted in public networks is bandlimited to 300-3400 Hz. Narrowband speech is lacking in the information from low-band (0-300Hz) and high-band (3.4-8kHz) that are found in wideband speech (0-8kHz). As a result, narrowband speech is characterized by the reduced intelligibility and muffled quality, and degraded speaker identification. Therefore a bandwidth extension method, one of the techniques providing wideband speech quality without any additional information has been explored. By using the conventional technique to recover the missing high-band speech, we estimated the spectral envelope and the excitation for high-band using statistical recovery and spectral folding, respectively. In addition, we proposed an approach that exploits the harmonic synthesis method for the reconstruction of low-band speech over the CELP speech coding. Spectral distortion measurement and listening test are performed to evaluate the proposed method. Evaluation with synthesized speech shows the quality improvement.

A source generator based modeling framework for synthesis of speech under stress

Abstract: The objective of this paper is to formulate an algorithm to generate stressed synthetic speech from neutral speech using a source generator framework previously employed for stressed speech recognition. The following goals are addressed (i) identify the most visible indicators of stress as perceived by the listener in stressed speaking styles such as loud, Lombard effect and angry, (ii) develop a mathematical model for representing speech production under stressed conditions, and (iii) employ the above model to produce emotional/stressed synthetic speech from neutral speech. The stress modeling scheme is applied to an existing low-bit rate CELP speech coder in order to investigate (i) the coder's ability and limitations reproducing stressed synthetic speech, and (ii) our ability to perturb coded neutral speech parameters at the synthesis stage so that resulting speech is perceived as being under stress. Two stress perturbation algorithms are proposed and evaluated. Results from formal listener evaluations show that 87% of neutral perturbed speech was indeed perceived as stressed.

High-quality vector adaptive transform coding at 4.8 kb/s

Abstract: A frequency-domain search procedure is presented that requires a computational load of approximately 1.5 MFLOPS, reduces the storage cost, and maintains the speech quality. A vector-excitation-coding (VXC) method in the transform domain called vector adaptive transform coding (VATC) is proposed. VATC provides a general framework for VXC in the transform domain, and the quality for the synthetic speech is close to that obtained in code-excited line prediction (CELP). The major advantages of VATC are the different ways of including information from previous frames, different perceptual distortion measurements, different transformations, efficient search algorithms, and the use of vector predictive quantization techniques so as to have a fully quantized coder at 4.8 kb/s. >

A robust algorithm for formant frequency extraction of noisy speech

Abstract: In this paper a new method for formant frequency estimation of noisy speech is proposed based on linear prediction analysis. Usually the linear prediction analysis based algorithms can extract the formant frequencies effectively for clean speech. When speech is corrupted by noise, however, their performance degrades seriously. It is well known that the autocorrelation function has the property of concentrating the energy of the white noise in the vicinity of the zero lag. Utilizing this property of the autocorrelation function, the proposed method extracts the formant frequencies from the autocorrelation function of the speech instead of the speech itself. The experimental results show that the proposed method is much more robust to noise than the conventional linear prediction based algorithms.