In speech system identification, linear predictive coding (LPC) model is often employed due to its simple yet powerful representation of speech production model. However, the accuracy of LPC model often depends on the number and quality of past speech samples that are fed into the model; and it becomes a problem when past speech samples are not widely available or corrupted by noise. In this paper, fuzzy system is integrated into the LPC model using the recursive least-squares approach, where the fuzzy parameters are used to characterize the given speech samples. This transformed domain LPC model is called the FRLS-LPC model, in which its performance depends on the fuzzy rules and membership functions defined by the user. Based on the simulations, the FRLS-LPC model with this special property is shown to outperform the LPC model. Under the condition of limited past speech samples, simulation result shows that the synthetic speech produced by the FRLS-LPC model is better than those produced by the LPC model in terms of prediction error. Furthermore with corrupted past speech samples, the FRLS-LPC model is able to provide better reconstructed speech while the LPC model is failed to do so.
Keyword:Fuzzy recursive least-squares Linear predictive coding Speech system identification
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Corresponding Author:Kah Wai Cheah, School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Pulau Pinang, Malaysia. Email: ckw13_mah028@student.usm.my
INTRODUCTIONIn speech processing, the parameterization of an analog speech signal is an important step, as the resulted parameters should represent the salient spectral energies of the sound [1]. Since the linear predictive coding (LPC) model provides a good approximation to the vocal tract spectral envelope in such a way that the parsimonious representation of the vocal tract characteristics becomes possible [2], the LPC model is the most common model used in speech spectral analysis. By changing spectral analysis in waveform data interval to spectrographic time-frequency domain where the information (such as inter-formant energy fill) can significantly be portrayed [3], the coefficients of LPC model prove its contributions in the application of speech signals synthesis [4]. In most recent decade, the LPC model has been implemented in various applications such as long term recordings of electromyography signals [5], recognition of Malayalam vowel [6], clustering of microarray genetic data [7], reconstruction of missing electrocardiogram signals [8], dynamic texture segmentation of image sequences [9], and classification of human activity based on microdoppler signatures [10]. All of these applications have proven that the LPC model could be implemented in a more general approach.However in speech processing, the accuracy of LPC model often depends on the number and quality of past speech samples that are fed into the model. Study shows that, the reasonable number of past speech samples that is...