Announcing the Electromagnetic Articulography (Day 1) Subset of the mngu0 Articulatory Corpus

This paper serves as an initial announcement of the availability of a corpus of articulatory data called mngu0. This corpus will ultimately consist of a collection of multiple sources of articulatory data acquired from a single speaker: electromagnetic articulography (EMA), audio, video, volumetric MRI scans, and 3D scans of dental impressions. This data will be provided free for research use. In this first stage of the release, we are making available one subset of EMA data, consisting of more than 1,300 phonetically diverse utterances recorded with a Carstens AG500 electromagnetic articulograph. Distribution of mngu0 will be managed by a dedicated “forum-style ” web site. This paper both outlines the general goals motivating the distribution of the data and the creation of the mngu0 web forum, and also provides a description of the EMA data contained in this initial release

The Electromagnetic Articulography Mandarin Accented English (EMA-MAE) Corpus of Acoustic and 3D Articulatory Kinematic Data

There is a significant need for more comprehensive electromagnetic articulography (EMA) datasets that can provide matched acoustics and articulatory kinematic data with good spatial and temporal resolution. The Marquette University Electromagnetic Articulography Mandarin Accented English (EMA-MAE) corpus provides kinematic and acoustic data from 40 gender and dialect balanced speakers representing 20 Midwestern standard American English L1 speakers and 20 Mandarin Accented English (MAE) L2 speakers, half Beijing region dialect and half are Shanghai region dialect. Three dimensional EMA data were collected at a 400 Hz sampling rate using the NDI Wave system, with articulatory sensors on the midsagittal lips, lower incisors, tongue blade and dorsum, plus lateral lip corner and tongue body. Sensors provide three-dimensional position data as well as two-dimensional orientation data representing the orientation of the sensor plane. Data have been corrected for head movement relative to a fixed reference sensor and also adjusted using a biteplate calibration system to place the data in an articulatory working space relative to each subject\u27s individual midsagittal and maxillary occlusal planes. Speech materials include isolated words chosen to focus on specific contrasts between the English and Mandarin languages, as well as sentences and paragraphs for continuous speech, totaling approximately 45 minutes of data per subject. A beta version of the EMA-MAE corpus is now available, and the full corpus is in preparation for public release to help advance research in areas such as pronunciation modeling, acoustic-articulatory inversion, L1-L2 comparisons, pronunciation error detection, and accent modification training

Artimate: an articulatory animation framework for audiovisual speech synthesis

We present a modular framework for articulatory animation synthesis using speech motion capture data obtained with electromagnetic articulography (EMA). Adapting a skeletal animation approach, the articulatory motion data is applied to a three-dimensional (3D) model of the vocal tract, creating a portable resource that can be integrated in an audiovisual (AV) speech synthesis platform to provide realistic animation of the tongue and teeth for a virtual character. The framework also provides an interface to articulatory animation synthesis, as well as an example application to illustrate its use with a 3D game engine. We rely on cross-platform, open-source software and open standards to provide a lightweight, accessible, and portable workflow.Comment: Workshop on Innovation and Applications in Speech Technology (2012

The magnetic resonance imaging subset of the mngu0 articulatory corpus

Author version contains correctly encoded (Unicode) fonts and attached multimedia content.International audienceThis paper announces the availability of the magnetic resonance imaging (MRI) subset of the mngu0 corpus, a collection of articulatory speech data from one speaker containing different modalities. This subset comprises volumetric MRI scans of the speaker's vocal tract during sustained production of vowels and consonants, as well as dynamic mid-sagittal scans of repetitive consonant-vowel (CV) syllable production. For reference, high-quality acoustic recordings of the speech material are also available. The raw data are made freely available for research purposes

Speech animation using electromagnetic articulography as motion capture data

Electromagnetic articulography (EMA) captures the position and orientation of a number of markers, attached to the articulators, during speech. As such, it performs the same function for speech that conventional motion capture does for full-body movements acquired with optical modalities, a long-time staple technique of the animation industry. In this paper, EMA data is processed from a motion-capture perspective and applied to the visualization of an existing multimodal corpus of articulatory data, creating a kinematic 3D model of the tongue and teeth by adapting a conventional motion capture based animation paradigm. This is accomplished using off-the-shelf, open-source software. Such an animated model can then be easily integrated into multimedia applications as a digital asset, allowing the analysis of speech production in an intuitive and accessible manner. The processing of the EMA data, its co-registration with 3D data from vocal tract magnetic resonance imaging (MRI) and dental scans, and the modeling workflow are presented in detail, and several issues discussed

Deep Architectures for Articulatory Inversion

We implement two deep architectures for the acousticarticulatory inversion mapping problem: a deep neural network and a deep trajectory mixture density network. We find that in both cases, deep architectures produce more accurate predictions than shallow architectures and that this is due to the higher expressive capability of a deep model and not a consequence of adding more adjustable parameters. We also find that a deep trajectory mixture density network is able to obtain better inversion accuracies than smoothing the results of a deep neural network. Our best model obtained an average root mean square error of 0.885 mm on the MNGU0 test dataset. Index Terms: Articulatory inversion, deep neural network, deep belief network, deep regression network, pretrainin

Ultrax:An Animated Midsagittal Vocal Tract Display for Speech Therapy

Speech sound disorders (SSD) are the most common communication impairment in childhood, and can hamper social development and learning. Current speech therapy interventions rely predominantly on the auditory skills of the child, as little technology is available to assist in diagnosis and therapy of SSDs. Realtime visualisation of tongue movements has the potential to bring enormous benefit to speech therapy. Ultrasound scanning offers this possibility, although its display may be hard to interpret. Our ultimate goal is to exploit ultrasound to track tongue movement, while displaying a simplified, diagrammatic vocal tract that is easier for the user to interpret. In this paper, we outline a general approach to this problem, combining a latent space model with a dimensionality reducing model of vocal tract shapes. We assess the feasibility of this approach using magnetic resonance imaging (MRI) scans to train a model of vocal tract shapes, which is animated using electromagnetic articulography (EMA) data from the same speaker. Index Terms: Ultrasound, speech therapy, vocal tract visualisation 1

Phoneme-to-Articulatory mapping using bidirectional gated RNN

International audienceDeriving articulatory dynamics from the acoustic speech signal has been addressed in several speech production studies. In this paper, we investigate whether it is possible to predict articulatory dynamics from phonetic information without having the acoustic speech signal. The input data may be considered as not sufficiently rich acoustically, as probably there is no explicit coarticulation information but we expect that the phonetic sequence provides compact yet rich knowledge. Motivated by the recent success of deep learning techniques used in the acoustic-to-articulatory inversion, we have experimented around the bidirectional gated recurrent neural network archi-tectures. We trained these models with an EMA corpus, and have obtained good performances similar to the state-of-the-art articulatory inversion from LSF features, but using only the phoneme labels and durations