Stabilised weighted linear prediction

Abstract

Weighted linear prediction (WLP) is a method to compute all-pole models of speech by applying temporal weighting of the square of the residual signal. By using short-time energy (STE) as a weighting function, this algorithm was originally proposed as an improved linear predictive (LP) method based on emphasising those samples that fit the underlying speech production model well. The original formulation of WLP, however, did not guarantee stability of all-pole models. Therefore, the current work revisits the concept of WLP by introducing a modified short-time energy function leading always to stable all-pole models. This new method, stabilised weighted linear prediction (SWLP), is shown to yield all-pole models whose general performance can be adjusted by properly choosing the length of the STE window, a parameter denoted by M .The study compares the performances of SWLP, minimum variance distortionless response (MVDR), and conventional LP in spectral modelling of speech corrupted by additive noise. The comparisons were performed by computing, for each method, the logarithmic spectral differences between the all-pole spectra extracted from clean and noisy speech in different segmental signal-to-noise ratio (SNR) categories. The results showed that the proposed SWLP algorithm was the most robust method against zero-mean Gaussian noise and the robustness was largest for SWLP with a small M -value. These findings were corroborated by a small listening test in which the majority of the listeners assessed the quality of impulse-train-excited SWLP filters, extracted from noisy speech, to be perceptually closer to original clean speech than the corresponding all-pole responses computed by MVDR. Finally, SWLP was compared to other short-time spectral estimation methods (FFT, LP, MVDR) in isolated word recognition experiments. Recognition accuracy obtained by SWLP, in comparison to other short-time spectral estimation methods, improved already at moderate segmental SNR values for sounds corrupted by zero-mean Gaussian noise. For realistic factory noise of low pass characteristics, the SWLP method improved the recognition results at segmental SNR levels below 0 dB.

Estimating the spectral tilt of the glottal source from telephone speech using a deep neural network

“…Three techniques-stabilized weighted linear prediction (SWLP), 13 double linear prediction (2LP), 4 and cepstral fit (CF)-were used as reference methods that all estimate the spectral tilt of the glottal source directly from speech.…”

Regularized All-Pole Models for Speaker Verification Under Noisy Environments

“…Besides the coding and synthesis applications, it has been noted that stabilization improves speaker verification performance as well [5]. Thus, stabilized WLP (SWLP) was proposed in [7]. In SWLP, the weighted autocorrelation matrix and the weighted autocorrelation vector are expressed as R swlp = Y T Y and r swlp = Y T y0, respectively (the original article [7] presents the problem in a slightly different form).…”

“…Thus, stabilized WLP (SWLP) was proposed in [7]. In SWLP, the weighted autocorrelation matrix and the weighted autocorrelation vector are expressed as R swlp = Y T Y and r swlp = Y T y0, respectively (the original article [7] presents the problem in a slightly different form). The columns of the matrix…”

“…In [5], the present authors extracted MFCCs from parametric all-pole spectral models based on linear prediction (LP) [6] and its temporally weighted extensions [7]. This led to increased speaker verification accuracy over the standard FFT method under additive noise contamination.…”

“…Therefore, we study the effects of different autocorrelation windowing methods on recognition accuracy. Finally, in addition to conventional LP, we extend regularization to the temporally weighted variants of LP, weighted LP (WLP) [5] and stabilized weighted linear prediction (SWLP) [7].…”