@InProceedings{8902667,
  author = {K. Funaki},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {TV-CAR speech analysis based on Regularized LP},
  year = {2019},
  pages = {1-5},
  abstract = {Linear Prediction (LP) analysis is speech analysis to estimate AR (Auto-Regressive) coefficients to represent the all-pole spectrum that is applied in speech synthesis recently besides speech coding. We have proposed l2-norm optimization based TV-CAR (Time-Varying Complex AR) speech analysis for an analytic signal, MMSE (Minimizing Mean Square Error) or ELS (Extended Least Square) method, and we have applied them into the speech processing such as robust ASR or F0 estimation of speech. On the other hand, B.Kleijn et al. have proposed Regularized Linear Prediction (RLP) method to suppress pitch related bias that is an overestimation of the first formant. In the RLP, l2-norm regularized term that is the norm of spectral changes in the frequencies is introduced to suppress the rapid spectral changes. The RLP estimates the parameter so as to minimize l2-norm criterion added by the l2-norm regularized penalty term. In this paper, the RLP-based TV-CAR speech analysis is proposed and evaluated with the F0 estimation of speech using IRAPT (Instantaneous RAPT) with Keele Pitch Database under noisy conditions.},
  keywords = {correlation methods;frequency estimation;least mean squares methods;speech coding;speech recognition;speech synthesis;time-varying systems;linear prediction analysis;Auto-Regressive;all-pole spectrum;speech synthesis;speech coding;mean square error minimization;0 estimation;Regularized Linear Prediction method;RLP-based TV-CAR speech analysis;time-varying complex AR;l2norm optimization based TV-CAR;Estimation;Mathematical model;Frequency estimation;Speech analysis;Analytical models;Speech coding;Speech processing;Time-Varying Complex AR (TV-CAR) analysis;Analytic signal;l2-norm regularization;F0 estimation of speech},
  doi = {10.23919/EUSIPCO.2019.8902667},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533023.pdf},
}

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On the other hand, B.Kleijn et al. have proposed Regularized Linear Prediction (RLP) method to suppress pitch related bias that is an overestimation of the first formant. In the RLP, l2-norm regularized term that is the norm of spectral changes in the frequencies is introduced to suppress the rapid spectral changes. The RLP estimates the parameter so as to minimize l2-norm criterion added by the l2-norm regularized penalty term. In this paper, the RLP-based TV-CAR speech analysis is proposed and evaluated with the F0 estimation of speech using IRAPT (Instantaneous RAPT) with Keele Pitch Database under noisy conditions.","keywords":"correlation methods;frequency estimation;least mean squares methods;speech coding;speech recognition;speech synthesis;time-varying systems;linear prediction analysis;Auto-Regressive;all-pole spectrum;speech synthesis;speech coding;mean square error minimization;0 estimation;Regularized Linear Prediction method;RLP-based TV-CAR speech analysis;time-varying complex AR;l2norm optimization based TV-CAR;Estimation;Mathematical model;Frequency estimation;Speech analysis;Analytical models;Speech coding;Speech processing;Time-Varying Complex AR (TV-CAR) analysis;Analytic signal;l2-norm regularization;F0 estimation of speech","doi":"10.23919/EUSIPCO.2019.8902667","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533023.pdf","bibtex":"@InProceedings{8902667,\n author = {K. Funaki},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {TV-CAR speech analysis based on Regularized LP},\n year = {2019},\n pages = {1-5},\n abstract = {Linear Prediction (LP) analysis is speech analysis to estimate AR (Auto-Regressive) coefficients to represent the all-pole spectrum that is applied in speech synthesis recently besides speech coding. We have proposed l2-norm optimization based TV-CAR (Time-Varying Complex AR) speech analysis for an analytic signal, MMSE (Minimizing Mean Square Error) or ELS (Extended Least Square) method, and we have applied them into the speech processing such as robust ASR or F0 estimation of speech. On the other hand, B.Kleijn et al. have proposed Regularized Linear Prediction (RLP) method to suppress pitch related bias that is an overestimation of the first formant. In the RLP, l2-norm regularized term that is the norm of spectral changes in the frequencies is introduced to suppress the rapid spectral changes. The RLP estimates the parameter so as to minimize l2-norm criterion added by the l2-norm regularized penalty term. 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