Dual estimation based vocal tract shape computation

This paper presents a new method for direct estimation of vocal tract shape from the speech signal. The method computes cross-sectional areas of uniform-length cylindrical tubes comprising the vocal tract. Cross-sectional areas are calculated from reflection coefficients at tube junctions whose values depend on the areas of adjoining tubes. A new state space representation of the speech production system has been formulated in which reflection coefficients are parameters. The state space model has been constructed using state equations of the glottal flow signal and vocal tract formulated from Liljencrants–Fant model and concatenated tube model respectively. Dual extended Kalman filtering algorithm has been used for estimation of unknown parameters of the system. The estimated reflection coefficients are then used to compute cross-sectional areas of the vocal tract. The performance of proposed technique has been compared to an existing shape estimation method proposed by Wakita. For both synthesized and natural speech signals, the performance of proposed method has been found to be comparable to the existing one. Nevertheless, the Kalman filter algorithm used in proposed method has provisions to tune measurement noise covariance which can be adjusted based on the noise level in speech. Therefore, the performance of proposed method has been seen to be comparatively more robust to noise than the existing technique.