Investigating Non-Uniqueness in the Acoustic-Articulatory Inversion Mapping

The task of inferring articulatory configurations from a given acoustic signal is a problem for which a reliable and accurate solution has been lacking for a number of decades. The changing shape of the vocal-tract is responsible for altering the parameters of sound. Each different configuration of articulators will regularly lead to a single distinct sound being produced (a unique mapping from the articulator to the acoustics). Therefore, it should be possible to take an acoustic signal and invert the process, giving the exact vocal-tract shape for a given sound. This would have wide-reaching applications in the field of speech and language technology, such as in improving facial animation and speech recognition systems. Using vocal-tract information inferred from the acoustic signal can facilitate a richer understanding of the actual constraints in articulator movement. However, research concerned with the inversion mapping has revealed that there is often a multi-valued mapping from the acoustic domain to the articulatory domain. Work in identifying and resolving this non-uniqueness thus far has been somewhat successful, with Mixture-Density Networks (MDN) and articulator trajectory systems presenting probabilistic methods of finding the most likely articulatory configuration for a given signal. Using an subset of an EMA corpus, along with a combination of an instantaneous inversion mapping and a non-parametric clustering algorithm, I aim to quantify the extent to which acoustically similar vectors to a given phone can exhibit qualitatively different vocal-tract shapes. Categorical identification of acoustically similar sounds that can have shown a multi-valued mapping in the articulatory domain, as well as identifying which articulators this occurs for, could be key to resolving issues in the reliability and quality of the inversion mapping

Estimating underlying articulatory targets of Thai vowels by using deep learning based on generating synthetic samples from a 3D vocal tract model and data augmentation

Representation learning is one of the fundamental issues in modeling articulatory-based speech synthesis using target-driven models. This paper proposes a computational strategy for learning underlying articulatory targets from a 3D articulatory speech synthesis model using a bi-directional long short-term memory recurrent neural network based on a small set of representative seed samples. From a seeding set, a larger training set was generated that provided richer contextual variations for the model to learn. The deep learning model for acoustic-to-target mapping was then trained to model the inverse relation of the articulation process. This method allows the trained model to map the given acoustic data onto the articulatory target parameters which can then be used to identify the distribution based on linguistic contexts. The model was evaluated based on its effectiveness in mapping acoustics to articulation, and the perceptual accuracy of speech reproduced from the estimated articulation. The results indicate that the model can accurately imitate speech with a high degree of phonemic precision

Deep learning assessment of syllable affiliation of intervocalic consonants

In English, a sentence like “He made out our intentions.” could be misperceived as “He may doubt our intentions.” because the coda /d/ sounds like it has become the onset of the next syllable. The nature and occurrence condition of this resyllabification phenomenon are unclear, however. Previous empirical studies mainly relied on listener judgment, limited acoustic evidence, such as voice onset time, or average formant values to determine the occurrence of resyllabification. This study tested the hypothesis that resyllabification is a coarticulatory reorganisation that realigns the coda consonant with the vowel of the next syllable. Deep learning in conjunction with dynamic time warping (DTW) was used to assess syllable affiliation of intervocalic consonants. The results suggest that convolutional neural network- and recurrent neural network-based models can detect cases of resyllabification using Mel-frequency spectrograms. DTW analysis shows that neural network inferred resyllabified sequences are acoustically more similar to their onset counterparts than their canonical productions. A binary classifier further suggests that, similar to the genuine onsets, the inferred resyllabified coda consonants are coarticulated with the following vowel. These results are interpreted with an account of resyllabification as a speech-rate-dependent coarticulatory reorganisation mechanism in speech

Statistical identification of articulatory roles in speech production.

The human speech apparatus is a rich source of information and offers many cues in the speech signal due to its biomechanical constraints and physiological interdependencies. Coarticulation, a direct consequence of these speech production factors, is one of the main problems affecting the performance of speech systems. Incorporation of production knowledge could potentially benefit speech recognisers and synthesisers. Hand coded rules and scores derived from the phonological knowledge used by production oriented models of speech are simple and incomplete representations of the complex speech production process. Statistical models built from measurements of speech articulation fail to identify the cause of constraints. There is a need for building explanatory yet descriptive models of articulation for understanding and modelling the effects of coarticulation. This thesis aims at providing compact descriptive models of realistic speech articulation by identifying and capturing the essential characteristics of human articulators using measurements from electro-magnetic articulography. The constraints on articulators during speech production are identified in the form of critical, dependent and redundant roles using entirely statistical and data-driven methods. The critical role captures the maximally constrained target driven behaviour of an articulator. The dependent role models the partial constraints due to physiological interdependencies. The redundant role reflects the unconstrained behaviour of an articulator which is maximally prone to coarticulation. Statistical target models are also obtained as the by-product of the identified roles. The algorithm for identification of articulatory roles (and estimation of respective model distributions) for each phone is presented and the results are critically evaluated. The identified data-driven constraints obtained are compared with the well known and commonly used constraints derived from the IPA (International Phonetic Alphabet). The identified critical roles were not only in agreement with the place and manner descriptions of each phone but also provided a phoneme to phone transformation by capturing language and speaker specific behaviour of articulators. The models trained from the identified constraints fitted better to the phone distributions (40% improvement) . The evaluation of the proposed search procedure with respect to an exhaustive search for identification of roles demonstrated that the proposed approach performs equally well for much less computational load. Articulation models built in the planning stage using sparse yet efficient articulatory representations using standard trajectory generation techniques showed some potential in modelling articulatory behaviour. Plenty of scope exists for further developing models of articulation from the proposed framework

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Registration and statistical analysis of the tongue shape during speech production

This thesis analyzes the human tongue shape during speech production. First, a semi-supervised approach is derived for estimating the tongue shape from volumetric magnetic resonance imaging data of the human vocal tract. Results of this extraction are used to derive parametric tongue models. Next, a framework is presented for registering sparse motion capture data of the tongue by means of such a model. This method allows to generate full three-dimensional animations of the tongue. Finally, a multimodal and statistical text-to-speech system is developed that is able to synthesize audio and synchronized tongue motion from text.Diese Dissertation beschäftigt sich mit der Analyse der menschlichen Zungenform während der Sprachproduktion. Zunächst wird ein semi-überwachtes Verfahren vorgestellt, mit dessen Hilfe sich Zungenformen von volumetrischen Magnetresonanztomographie- Aufnahmen des menschlichen Vokaltrakts schätzen lassen. Die Ergebnisse dieses Extraktionsverfahrens werden genutzt, um ein parametrisches Zungenmodell zu konstruieren. Danach wird eine Methode hergeleitet, die ein solches Modell nutzt, um spärliche Bewegungsaufnahmen der Zunge zu registrieren. Dieser Ansatz erlaubt es, dreidimensionale Animationen der Zunge zu erstellen. Zuletzt wird ein multimodales und statistisches Text-to-Speech-System entwickelt, das in der Lage ist, Audio und die dazu synchrone Zungenbewegung zu synthetisieren.German Research Foundatio

Exploiting phonological constraints for handshape recognition in sign language video

The ability to recognize handshapes in signing video is essential in algorithms for sign recognition and retrieval. Handshape recognition from isolated images is, however, an insufficiently constrained problem. Many handshapes share similar 3D configurations and are indistinguishable for some hand orientations in 2D image projections. Additionally, significant differences in handshape appearance are induced by the articulated structure of the hand and variants produced by different signers. Linguistic rules involved in the production of signs impose strong constraints on the articulations of the hands, yet, little attention has been paid towards exploiting these constraints in previous works on sign recognition. Among the different classes of signs in any signed language, lexical signs constitute the prevalent class. Morphemes (or, meaningful units) for signs in this class involve a combination of particular handshapes, palm orientations, locations for articulation, and movement type. These are thus analyzed by many sign linguists as analogues of phonemes in spoken languages. Phonological constraints govern the ways in which phonemes combine in American Sign Language (ASL), as in other signed and spoken languages; utilizing these constraints for handshape recognition in ASL is the focus of the proposed thesis. Handshapes in monomorphemic lexical signs are specified at the start and end of the sign. The handshape transition within a sign are constrained to involve either closing or opening of the hand (i.e., constrained to exclusively use either folding or unfolding of the palm and one or more fingers). Furthermore, akin to allophonic variations in spoken languages, both inter- and intra- signer variations in the production of specific handshapes are observed. We propose a Bayesian network formulation to exploit handshape co-occurrence constraints also utilizing information about allophonic variations to aid in handshape recognition. We propose a fast non-rigid image alignment method to gain improved robustness to handshape appearance variations during computation of observation likelihoods in the Bayesian network. We evaluate our handshape recognition approach on a large dataset of monomorphemic lexical signs. We demonstrate that leveraging linguistic constraints on handshapes results in improved handshape recognition accuracy. As part of the overall project, we are collecting and preparing for dissemination a large corpus (three thousand signs from three native signers) of ASL video annotated with linguistic information such as glosses, morphological properties and variations, and start/end handshapes associated with each ASL sign

A systematic investigation of gesture kinematics in evolving manual languages in the lab

Item does not contain fulltextSilent gestures consist of complex multi-articulatory movements but are now primarily studied through categorical coding of the referential gesture content. The relation of categorical linguistic content with continuous kinematics is therefore poorly understood. Here, we reanalyzed the video data from a gestural evolution experiment (Motamedi, Schouwstra, Smith, Culbertson, & Kirby, 2019), which showed increases in the systematicity of gesture content over time. We applied computer vision techniques to quantify the kinematics of the original data. Our kinematic analyses demonstrated that gestures become more efficient and less complex in their kinematics over generations of learners. We further detect the systematicity of gesture form on the level of thegesture kinematic interrelations, which directly scales with the systematicity obtained on semantic coding of the gestures. Thus, from continuous kinematics alone, we can tap into linguistic aspects that were previously only approachable through categorical coding of meaning. Finally, going beyond issues of systematicity, we show how unique gesture kinematic dialects emerged over generations as isolated chains of participants gradually diverged over iterations from other chains. We, thereby, conclude that gestures can come to embody the linguistic system at the level of interrelationships between communicative tokens, which should calibrate our theories about form and linguistic content.29 p