Cyclic autocorrelation-based linear prediction analysis of speech
نویسندگان
چکیده
In this paper, a new approach for linear prediction (LP) analysis is proposed. This approach makes the assumption that the speech signal is cyclostationary and uses cyclic autocorrelation function for computing LP parameters. Since the cyclic autocorrelation function of a stationary random signal is zero, independent of its statistical description, this analysis is robust to additive noise, white or colored. It is applied to speech recognition. Preliminary results demonstrate its robustness to white additive noise.
منابع مشابه
Estimation of LPC coefficients using Evolutionary Algorithms
The vast use of Linear Prediction Coefficients (LPC) in speech processing systems has intensified the importance of their accurate computation. This paper is concerned with computing LPC coefficients using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Dif-ferential Evolution (DE) and Particle Swarm Optimization with Differentially perturbed Velocity (PSO-DV...
متن کاملA geostatistical model for linear prediction analysis of speech
This paper presents a geostatistical model as a new approach to the linear prediction analysis of speech. The autocorrelation method of autoregressive modeling, which is widely applied in the linear predictive coding of speech, is used as a benchmark for comparison with the present algorithm. Before discussing the proposed model, we will briefly describe the concepts of linear prediction analys...
متن کاملExtended weighted linear prediction using the autocorrelation snapshot - a robust speech analysis method and its application to recognition of vocal emotions
Temporally weighted linear predictive methods have recently been successfully used for robust feature extraction in speech and speaker recognition. This paper introduces their general formulation, where various efficient temporal weighting functions can be included in the optimization of the all-pole coefficients of a linear predictive model. Temporal weighting is imposed by multiplying element...
متن کاملVoice activity detection in degraded speech using excitation source information
This paper proposes a method for detection of voiced regions from speech signals collected in noisy environment. The proposed method is based on the characteristics of excitation source of speech production. The degraded speech signal is processed by linear prediction analysis for deriving the linear prediction residual. Hilbert envelope of the linear prediction residual is processed using cova...
متن کاملRole of Different Order Ranges of Autocorrelation Sequence on the Performance of Speech Recognition
In this paper, cepstral features derived from the Differentiated Relative Higher Order Autocorrelation Sequence Spectrum (DRHOASS) are proposed for improving the robustness of a speech recognizer in the presence of background noise. Proposed method is analyzed and compared in terms of the autocorrelation coefficients they employ with the traditional feature extraction methods based on Linear Pe...
متن کامل