A new entropy bound with low computational complexity for differential Shannon entropy estimation with kernel density approach is proposed in this study, which is based on defining a bound for the Kullback-Leibler divergence between two Gaussian mixture models. The proposed entropy bound is derived to provide computational efficiency without decreasing the accuracy in detecting heart sound segments in respiratory sound. It is shown both theoretically and experimentally that using the proposed bound in an adaptive threshold-based detection method gives very similar performance compared to that obtained by a nonparametric kernel based approach, while its computational cost is much lower. The performance of the proposed method is shown and compared with the three methods in the literature by means of experiments utilizing a database of 20 subjects. The results show that the false negative rate values for the proposed method are 1.45±1.50 % and 1.98±1.81 % for low and medium flow rates, respectively. These average values are similar to the results obtained by the alternative methods. Moreover, the average elapsed time of the proposed method for a piece of data with a length of 20 s is 0.05 s, which is significantly lower than that of the other methods.

This paper presents a complete framework for articulatory inversion based on jump Markov linear systems (JMLS). In the model, the acoustic measurements and the position of each articulator are considered as observable measurement and continuous-valued hidden state of the system, respectively, and discrete regimes of the system are represented by the use of a discrete-valued hidden modal state. Articulatory inversion based on JMLS involves learning the model parameter set of the system and making inference about the state (position of each articulator) of the system using acoustic measurements. Iterative learning algorithms based on maximum-likelihood (ML) and maximum a posteriori (MAP) criteria are proposed to learn the model parameter set of the JMLS. It is shown that the learning procedure of the JMLS is a generalized version of hidden Markov model (HMM) training when both acoustic and articulatory data are given. In this paper, it is shown that the MAP-based learning algorithm improves modeling performance of the system and gives significantly better results compared to ML. The inference stage of the proposed algorithm is based on an interacting multiple models (IMM) approach, and done online (filtering), and/or offline (smoothing). Formulas are provided for IMM-based JMLS smoothing. It is shown that smoothing significantly improves the performance of articulatory inversion compared to filtering. Several experiments are conducted with the MOCHA database to show the performance of the proposed method. Comparison of the performance of the proposed method with the ones given in the literature shows that the proposed method improves the performance of state space approaches, making state space approaches comparable to the best published results.

This paper presents a detailed framework for Gaussian mixture model (GMM)-based articulatory inversion equipped with special postprocessing smoothers, and with the capability to perform audio-visual information fusion. The effects of different acoustic features on the GMM inversion performance are investigated and it is shown that the integration of various types of acoustic (and visual) features improves the performance of the articulatory inversion process. Dynamic Kalman smoothers are proposed to adapt the cutoff frequency of the smoother to data and noise characteristics; Kalman smoothers also enable the incorporation of auxiliary information such as phonetic transcriptions to improve articulatory estimation. Two types of dynamic Kalman smoothers are introduced: global Kalman (GK) and phoneme-based Kalman (PBK). The same dynamic model is used for all phonemes in the GK smoother; it is shown that GK improves the performance of articulatory inversion better than the conventional low-pass (LP) smoother. However, the PBK smoother, which uses one dynamic model for each phoneme, gives significantly better results than the GK smoother. Different methodologies to fuse the audio and visual information are examined. A novel modified late fusion algorithm, designed to consider the observability degree of the articulators, is shown to give better results than either the early or the late fusion methods. Extensive experimental studies are conducted with the MOCHA database to illustrate the performance gains obtained by the proposed algorithms. The average RMS error and correlation coefficient between the true (measured) and the estimated articulatory trajectories are 1.227 mm and 0.868 using audiovisual information fusion and GK smoothing, and 1.199 mm and 0.876 using audiovisual information fusion together with PBK smoothing based on a phonetic transcription of the utterance.

In this paper we present a technique for obtaining Vocal Tract (VT) time functions from the acoustic speech signal. Knowledge-based Acoustic Parameters (APs) are extracted from the speech signal and a pertinent subset is used to obtain the mapping between them and the VT time functions. Eight different vocal tract constriction variables consisting of five constriction degree variables, lip aperture (LA), tongue body (TBCD), tongue tip (TTCD), velum (VEL), and glottis (GLO); and three constriction location variables, lip protrusion (LP), tongue tip (TTCL), tongue body (TBCL) were considered in this study. The TAsk Dynamics Application model (TADA [1]) is used to create a synthetic speech dataset along with its corresponding VT time functions. We explore Support Vector Regression (SVR) followed by Kalman smoothing to achieve mapping between the APs and the VT time functions.

This work examines the utility of formant frequencies and their energies in acoustic-to-articulatory inversion. For this purpose, formant frequencies and formant spectral amplitudes are automatically estimated from audio, and are treated as observations for the purpose of estimating electromagnetic articulography (EMA) coil positions. A mixture Gaussian regression model with mel-frequency cepstral (MFCC) observations is modified by using formants and energies to either replace or augment the MFCC observation vector. The augmented observation results in 3.4 % lower RMS error, and 2.7 % higher correlation coefficient, than the baseline MFCC observation. Improvement is especially good for plosive consonants, possibly because formant tracking provides information about the acoustic resonances that would be otherwise unavailable during plosive closure and release. Index Terms: acoustic-to-articulatory inversion, formant tracking, GMM regression

In this paper, we propose a new approach for dynamic speech spectrum representation and tracking vocal tract resonance (VTR) frequencies. The method involves representing the spectral density of the speech signals as a mixture of Gaussians with unknown number of components for which time-varying Dirichlet process mixture model (DPM) is utilized. In the resulting representation, the number of formants is allowed to vary in time. The paper first presents an analysis on the continuity of the formants in the spectrum during the speech utterance. The analysis is based on a new state space representation of concatenated tube model. We show that the number of formants which appear in the spectrum is directly related to the location of the constriction of the vocal tract (i.e., the location of the excitation). Moreover, the disappearance of the formants in the spectrum is explained by “uncontrollable modes” of the state space model. Under the assumption of existence of varying number of formants in the spectrum, we propose the use of a DPM model based multi-target tracking algorithm for tracking unknown number of formants. The tracking algorithm defines a hierarchical Bayesian model for the unknown formant states and the inference is done via Rao–Blackwellized particle filter.

This paper presents a systematic framework for accurate estimation of vocal tract resonances (formants) using neither training data nor a phonetic transcription. In the proposed method, the speech signal is segmented in voiced and unvoiced parts and the resonance frequencies of the vocal tract are estimated by dynamic programming and further processed by using Kalman filtering/smoothing for each part. The performance of the proposed method is compared with three different methods which are baseline, WaveSurfer and MSR. The proposed method reduces the overall vocal tract resonances (for F1, F2 and F3) estimation error rate by 35%, 39.6% and 2.74% over the baseline, WaveSurfer and MSR methods respectively.