Speech Reconstruction by Sparse Linear Prediction
Koloda J, Peinado A, Sánchez V (2012)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2012
Publisher: Springer
Pages Range: 247-256
Event location: Madrid
ISBN: 978-3-642-35292-8
DOI: 10.1007/978-3-642-35292-8_26
Abstract
This paper proposes a new variant of the least square autoregressive (LSAR) method for speech reconstruction, which can estimate via least squares a segment of missing samples by applying the linear prediction (LP) model of speech. First, we show that the use of a single high-order linear predictor can provide better results than the classic LSAR techniques based on short- and long-term predictors without the need of a pitch detector. However, this high-order predictor may reduce the reconstruction performance due to estimation errors, especially in the case of short pitch periods, and non-stationarity. In order to overcome these problems, we propose the use of a sparse linear predictor which resembles the classical speech model, based on short- and long-term correlations, where many LP coefficients are zero. The experimental results show the superiority of the proposed approach in both signal to noise ratio and perceptual performance.
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How to cite
APA:
Koloda, J., Peinado, A., & Sánchez, V. (2012). Speech Reconstruction by Sparse Linear Prediction. In Proceedings of the Advances in Speech and Language Technologies for Iberian Languages (IberSPEECH) (pp. 247-256). Madrid, ES: Springer.
MLA:
Koloda, Jan, A.M. Peinado, and V. Sánchez. "Speech Reconstruction by Sparse Linear Prediction." Proceedings of the Advances in Speech and Language Technologies for Iberian Languages (IberSPEECH), Madrid Springer, 2012. 247-256.
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