This paper explores statistical tools that help analyze the predictability in the acoustic-to-articulatory inversion of speech, using an Electromagnetic Articulography database of simultaneously recorded acoustic and articulatory data. Since it has been shown that speech acoustics can be mapped to non-unique articulatory modes, the variance of the articulatory parameters is not sufficient to understand the predictability of the inverse mapping. We, therefore, estimate an upper bound to the conditional entropy of the articulatory distribution. This provides a probabilistic estimate of the range of articulatory values (either over a continuum or over discrete non-unique regions) for a given acoustic vector in the database. The analysis is performed for different British/Scottish English consonants with respect to which articulators (lips, jaws or the tongue) are important for producing the phoneme. The paper shows that acoustic-articulatory mappings for the important articulators have a low upper bound on the entropy, but can still have discrete non-unique configurations.

This paper studies the role of non-uniqueness in the Acoustic-to-Articulatory Inversion. It is generally believed that applying continuity constraints to the estimates of the articulatory parameters can resolve the problem of non-uniqueness. This paper tries to find out whether all instances of non-uniqueness can be resolved using continuity constraints. The investigation reveals that applying continuity constraints provides the best estimate in roughly around 50 to 53% of the non-unique mappings. Roughly around 8 to 13% of the non-unique mappings are best estimated by choosing discontinuous paths along the hypothetical high probability estimates of articulatory trajectories.

Abstract This paper discusses a model which conceptually demonstrateshow infants could learn the normalization between infant-adultacoustics. Themodelproposesthatthemappingcanbeinferredfrom the topological correspondences between the adult and in-fant acoustic spaces, that are clustered separately in an unsu-pervised manner. The model requires feedback from the adultin order to select the right topology for clustering, which is acrucial aspect of the model. The feedback is in terms of anoverall rating of the imitation effort by the infant, rather thana frame-by-frame correspondence. Using synthetic, but contin-uous speech data, we demonstrate that clusters, which have agood topological correspondence, are perceived to be similarby a phonetically trained listener.IndexTerms: infantspeechacquisition, unsupervisedlearning,self organizing maps 1. Introduction An infant learning to communicate with speech has been asource of intrigue and interest, both from the point of view ofpsychology and medicine, and from the point of view of speechresearch. Understanding this phenomenon is especially diffi-cult, given that infants do not remember the process once theygrowupandtheremaybelimitedmeansofcommunicatingwithinfants while the process of learning takes place.There are several challenges an infant faces when tryingto acquire the ability to speak. The first and the most impor-tant challenge is learning the sensorimotor mapping betweenthe acoustics and the articulatory configurations [1, 2, 3]. Mostofthetheoriesintheabovestudiesmakeuseofthephenomenonofbabblingtoexplainthisprocess. Secondly, aninfantlearningto speak needs to learn how to categorize the different soundsthat he/she produces or hears from the adults [4, 5].An infant also needs to understand how to correlate the dif-ferentsoundsproducedduringbabblingtothesoundspresentinadult speech which is the main focus of our study. There havebeen several proposals about which acoustic correlates are in-variant between infant and adult speech (e.g., [6]). These mea-sureswereevaluatedby[7]and[8]andwereshowntobeusefulinacquiringtheabilitytoimitatethesoundsproducedbyadults.However, are these invariant acoustic features that normalizeadult speech, intrinsically and instinctively known to children,or do they learnt it? While most studies have assumed this to beinnate to children, Plummer

This paper proposes a fundamental frequency tracker, with the specific purpose of comparing the automatic estimates with pitch contours that are sketched by trained phoneticians. The method uses a frequency domain approach to estimate pitch tracks that form minimum jerk trajectories. This method tries to mimic motor movements of the hand made while sketch-ing. When the fundamental frequency tracked by the proposed method on the oral and laryngograph signals were compared using the MOCHA-TIMIT database, the correlation was 0.98 and the root mean squared error was 4.0 Hz, which was slightly better than a state-of-the-art pitch tracking algorithm included in the ESPS. We also demonstrate how the proposed algorithm could to be applied when comparing with sketches made by phoneticians for the variations in accent II among the Swedish dialects. Index Terms: pitch tracking, Constant-Q, Swedish accent II 1.

In order to study inter-speaker variability, this work aims to assess the generalization capabilities of data-based multi-speaker articulatory models. We use various three-mode factor analysis techniques to model the variations of midsagittal vocal tract contours obtained from MRI images for three French speakers articulating 73 vowels and consonants. Articulations of a given speaker for phonemes not present in the training set are then predicted by inversion of the models from measurements of these phonemes articulated by the other subjects. On the average, the prediction RMSE was 5.25 mm for tongue contours, and 3.3 mm for 2D midsagittal vocal tract distances. Besides, this study has established a methodology to determine the optimal number of factors for such models.

* names in alphabetical order This paper presents an Acoustic-to-Articulatory inversion method based on local regression. Two types of local regression, a non-parametric and a local linear regression have been applied on a corpus containing simultaneous recordings of positions of articulators and the corresponding acoustics. A maximum likelihood trajectory smoothing using the estimated dynamics of the articulators is also applied on the regression estimates. The average root mean square error in estimating articulatory positions, given the acoustics, is 1.56 mm for the nonparametric regression and 1.52 mm for the local linear regression. The local linear regression is found to perform significantly better than regression using Gaussian Mixture Models using the same acoustic and articulatory features.

This paper explores the possibility and extent of non-uniqueness in the acoustic-to-articulatory inversion of speech, from a statistical point of view. It proposes a technique to estimate the non-uniqueness, based on finding peaks in the conditional probability function of the articulatory space. The paper corroborates the existence of non-uniqueness in a statistical sense, especially in stop consonants, nasals and fricatives. The relationship between the importance of the articulator position and nonuniqueness at each instance is also explored. Index Terms: acoustic-to-articulatory inversion, nonuniqueness, gaussian mixture modeling.

The paper proposes a general version of a coupled Hidden Markov/Bayesian Network model for performing phoneme recognition on acoustic-articulatory data. The model uses knowledge learned from the articulatory measurements, available for training, for phoneme recognition on the acoustic input. After training on the articulatory data, the model is able to predict 71.5 % of the articulatory state sequences using the acoustic input. Using optimized parameters, the proposed method shows a slight improvement for two speakers over the baseline phoneme recognition system which does not use articulatory knowledge. However, the improvement is only statistically significant for one of the speakers. While there is an improvement in recognition accuracy for the vowels, diphthongs and to some extent the semi-vowels, there is a decrease in accuracy for the remaining phonemes. Index Terms: phoneme recognition, articulatory measurements, Coupled-HMM.

This paper suggests a scheme for classifying online handwritten characters, based on dynamic space warping of strokes within the characters. A method for segmenting components into strokes using velocity profiles is proposed. Each stroke is a simple arbitrary shape and is encoded using three attributes. Correspondence between various strokes is established using Dynamic Space Warping. A distance measure which reliably differentiates between two corresponding simple shapes (strokes) has been formulated thus obtaining a perceptual distance measure between any two characters. Tests indicate an accuracy of over 85% on two different datasets of characters.

This paper studies the hypothesis that the acoustic-toarticulatory mapping is non-unique, statistically. The distributions of the acoustic and articulatory spaces are obtained by fitting the data into a Gaussian Mixture Model. The kurtosis is used to measure the non-Gaussianity of the distributions and the Bhattacharya distance is used to find the difference between distributions of the acoustic vectors producing non-unique articulator configurations. It is found that stop consonants and alveolar fricatives are generally not only non-linear but also nonunique, while dental fricatives are found to be highly non-linear but fairly unique. Two more investigations are also discussed: the first is on how well the best possible piecewise linear regression is likely to perform, the second is on whether the dynamic constraints improve the ability to predict different articulatory regions corresponding to the same region in the acoustic space.