| Paper: | SLP-P21.1 | ||
| Session: | Speech Detection, Enhancement and Analysis | ||
| Time: | Friday, May 19, 16:30 - 18:30 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Speech Analysis | ||
| Title: | Blind Identification of Non-Gaussian Autoregressive Models for Efficient Analysis of Speech Signals | ||
| Authors: | Chunjian Li, Søren Vang Andersen, Aalborg University, Denmark | ||
| Abstract: | Speech signals, especially voiced speech, can be better modeled by non-Gaussian autoregressive (AR) models than by Gaussian ones. Non-Gaussian AR estimators are usually highly non-linear and computationally prohibitive. This paper presents an efficient algorithm that jointly estimates the AR parameters and the excitation statistics and dynamics of voiced speech signals. A model called the Hidden Markov-Autoregressive model (HMARM) is designed for this purpose. The HMARM models the excitation to the AR model using a Hidden Markov Model with two Gaussian states that have, respectively, a small and a large mean but identical variances. This formulation enables a computationally efficient exact EM algorithm to learn all parameters jointly, instead of resorting to pure numerical optimization or relaxed EM algorithms. The algorithm converges in typically 3 to 5 iterations. Experimental results show that the estimated AR parameters have much lower bias and variance than the conventional Least Squares solution.We also show that the new estimator has a very good shift-invariance property that is useful in many applications. | ||
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