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The glossectomy procedure, involving surgical resection of cancerous lingual tissue, has long been observed to affect speech production. This study aims to quantitatively index and compare complexity of vocal tract shaping due to lingual movement in individuals who have undergone glossectomy and typical speakers using real-time magnetic resonance imaging data and Principal Component Analysis. The data reveal that (i) the type of glossectomy undergone largely predicts the patterns in vocal tract shaping observed, (ii) gross forward and backward motion of the tongue body accounts for more change in vocal tract shaping than do subtler movements of the tongue (e.g., tongue tip constrictions) in patient data, and (iii) fewer vocal tract shaping components are required to account for the patients' speech data than typical speech data, suggesting that the patient data at hand exhibit less complex vocal tract shaping in the midsagittal plane than do the data from the typical speakers observed. [ABSTRACT FROM AUTHOR]

It has been previously observed [McMicken, Salles, Berg, Vento-Wilson, Rogers, Toutios, and Narayanan. (2017). J. Commun. Disorders, Deaf Stud. Hear. Aids 5(2), 1–6] using real-time magnetic resonance imaging that a speaker with severe congenital tongue hypoplasia (aglossia) had developed a compensatory articulatory strategy where she, in the absence of a functional tongue tip, produced a plosive consonant perceptually similar to /d/ using a bilabial constriction. The present paper provides an updated account of this strategy. It is suggested that the previously observed compensatory bilabial closing that occurs during this speaker's /d/ production is consistent with vocal tract shaping resulting from hyoid raising created with mylohyoid action, which may also be involved in typical /d/ production. Simulating this strategy in a dynamic articulatory synthesis experiment leads to the generation of /d/-like formant transitions. [ABSTRACT FROM AUTHOR]

In speech production, the motor system organizes articulators such as the jaw, tongue, and lips into synergies whose function is to produce speech sounds by forming constrictions at the phonetic places of articulation. The present study tests whether synergies for different constriction tasks differ in terms of inter-articulator coordination. The test is conducted on utterances [ɑpɑ], [ɑtɑ], [ɑiɑ], and [ɑkɑ] with a real-time magnetic resonance imaging biomarker that is computed using a statistical model of the forward kinematics of the vocal tract. The present study is the first to estimate the forward kinematics of the vocal tract from speech production data. Using the imaging biomarker, the study finds that the jaw contributes least to the velar stop for [k], more to pharyngeal approximation for [ɑ], still more to palatal approximation for [i], and most to the coronal stop for [t]. Additionally, the jaw contributes more to the coronal stop for [t] than to the bilabial stop for [p]. Finally, the study investigates how this pattern of results varies by participant. The study identifies differences in inter-articulator coordination by constriction task, which support the claim that inter-articulator coordination differs depending on the active articulator synergy. [ABSTRACT FROM AUTHOR]

We address the hypothesis that postures adopted during grammatical pauses in speech production are more “mechanically advantageous” than absolute rest positions for facilitating efficient postural motor control of vocal tract articulators. We quantify vocal tract posture corresponding to inter-speech pauses, absolute rest intervals as well as vowel and consonant intervals using automated analysis of video captured with real-time magnetic resonance imaging during production of read and spontaneous speech by 5 healthy speakers of American English. We then use locally-weighted linear regression to estimate the articulatory forward map from low-level articulator variables to high-level task/goal variables for these postures. We quantify the overall magnitude of the first derivative of the forward map as a measure of mechanical advantage. We find that postures assumed during grammatical pauses in speech as well as speech-ready postures are significantly more mechanically advantageous than postures assumed during absolute rest. Further, these postures represent empirical extremes of mechanical advantage, between which lie the postures assumed during various vowels and consonants. Relative mechanical advantage of different postures might be an important physical constraint influencing planning and control of speech production. [ABSTRACT FROM AUTHOR]

This paper presents an automatic procedure to analyze articulatory setting in speech production using real-time magnetic resonance imaging of the moving human vocal tract. The procedure extracts frames corresponding to inter-speech pauses, speech-ready intervals and absolute rest intervals from magnetic resonance imaging sequences of read and spontaneous speech elicited from five healthy speakers of American English and uses automatically extracted image features to quantify vocal tract posture during these intervals. Statistical analyses show significant differences between vocal tract postures adopted during inter-speech pauses and those at absolute rest before speech; the latter also exhibits a greater variability in the adopted postures. In addition, the articulatory settings adopted during inter-speech pauses in read and spontaneous speech are distinct. The results suggest that adopted vocal tract postures differ on average during rest positions, ready positions and inter-speech pauses, and might, in that order, involve an increasing degree of active control by the cognitive speech planning mechanism. [ABSTRACT FROM AUTHOR]

Abstract: We present and evaluate two statistical methods for estimating kinematic relationships of the speech production system: artificial neural networks and locally-weighted regression. The work is motivated by the need to characterize this motor system, with particular focus on estimating differential aspects of kinematics. Kinematic analysis will facilitate progress in a variety of areas, including the nature of speech production goals, articulatory redundancy and, relatedly, acoustic-to-articulatory inversion. Statistical methods must be used to estimate these relationships from data since they are infeasible to express in closed form. Statistical models are optimized and evaluated – using a heldout data validation procedure – on two sets of synthetic speech data. The theoretical and practical advantages of both methods are also discussed. It is shown that both direct and differential kinematics can be estimated with high accuracy, even for complex, nonlinear relationships. Locally-weighted regression displays the best overall performance, which may be due to practical advantages in its training procedure. Moreover, accurate estimation can be achieved using only a modest amount of training data, as judged by convergence of performance. The algorithms are also applied to real-time MRI data, and the results are generally consistent with those obtained from synthetic data. [Copyright &y& Elsevier]

Speech can be represented as a constellation of constricting vocal tract actions called gestures, whose temporal patterning with respect to one another is expressed in a gestural score. Current speech datasets do not come with gestural annotation and no formal gestural annotation procedure exists at present. This paper describes an iterative analysis-by-synthesis landmark-based time-warping architecture to perform gestural annotation of natural speech. For a given utterance, the Haskins Laboratories Task Dynamics and Application (TADA) model is employed to generate a corresponding prototype gestural score. The gestural score is temporally optimized through an iterative timing-warping process such that the acoustic distance between the original and TADA-synthesized speech is minimized. This paper demonstrates that the proposed iterative approach is superior to conventional acoustically-referenced dynamic timing-warping procedures and provides reliable gestural annotation for speech datasets. [ABSTRACT FROM AUTHOR]

Formant dispersion, the average spacing, in Hz, between the resonant frequencies of a vocalization, has been predicted to provide honest information about signaler body size. Previous descriptions of black and white colobus monkey ( Colobus guereza, “guereza”) roars, however, suggest a formant dispersion that is far lower than expected for an animal its size, and could effectively exaggerate its body size. Nonetheless, recent research on red deer shows that even when the formant positions of vocalizations effectively exaggerate body size, they may still provide honest cues within a species. We investigate whether the frequency bands observed in the spectrograms of guereza roars represent formants, and whether roar formant dispersion and/or individual formants provide honest information about body size (specifically, body mass) relative to conspecifics, although perhaps not relative to other species. We document coordinated vertical movements in the frequency bands of guereza roars and show that these bands move independently of fundamental frequency, indicating that they represent formants. We show, for captive adult male guerezas, that signaler body mass significantly predicts roar formant dispersion, even for randomly selected calls. Body mass also predicts formants 2 and 3, but the relationships are not as strong as with formant dispersion. Our roar formant dispersion calculations predict a vocal tract length of approx. 29 cm, but anatomically determined guereza vocal tract length is much smaller: approx. 7–8 cm. Videotaped roars revealed no laryngeal movement during roars, but rather inflation of the subhyoid air sac. We measured the volume of this air sac (approx. 10 cm3) and speculate that it may function in roars to exaggerate body size, relative to other species. [ABSTRACT FROM AUTHOR]

Recent advances in real-time magnetic resonance imaging (RT-MRI) have made it possible to study the anatomy and dynamic motion of the vocal tract during speech production with great detail. The abundance of rich data on speech articulation provided by medical imaging techniques affords new opportunities for speech science, linguistics, clinical and technological research and application development, but also presents new challenges in audio–video data analysis and data modeling. We review techniques used in analysis of articulatory data acquired using RT-MRI, and assess the utility of different approaches for different types of data and research goals. [ABSTRACT FROM AUTHOR]

Abstract: Motor theories, which postulate that speech perception is related to linguistically significant movements of the vocal tract, have guided speech perception research for nearly four decades but have had little impact on automatic speech recognition. In this paper, we describe a signal processing technique named MIMICRI that may help link motor theory with automatic speech recognition by providing a practical approach to recovering articulator positions from acoustics. MIMICRI’s name reflects three important operations it can perform on time-series data: it can reduce the dimensionality of a data set (manifold inference); it can blindly invert nonlinear functions applied to the data (mapping inversion); and it can use temporal context to estimate intermediate data (contextual recovery of information). In order for MIMICRI to work, the signals to be analyzed must be functions of unobservable signals that lie on a linear subspace of the set of all unobservable signals. For example, MIMICRI will typically work if the unobservable signals are band-pass and we know the pass-band, as is the case for articulator motions. We discuss the abilities of MIMICRI as they relate to speech processing applications, particularly as they relate to inverting the mapping from speech articulator positions to acoustics. We then present a mathematical proof that explains why MIMICRI can invert nonlinear functions, which it can do even in some cases in which the mapping from the unobservable variables to the observable variables is many-to-one. Finally, we show that MIMICRI is able to infer accurately the positions of the speech articulators from speech acoustics for vowels. Five parameters estimated by MIMICRI were more linearly related to articulator positions than 128 spectral energies. [Copyright &y& Elsevier]