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

Parallel Reference Speaker Weighting for Kinematic-Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and open problem. This paper proposes a novel approach to acoustic-to-articulatory inversion, Parallel Reference Speaker Weighting (PRSW), which requires no kinematic data for the target speaker and a small amount of acoustic adaptation data. PRSW hypothesizes that acoustic and kinematic similarities are correlated and uses speaker-adapted articulatory models derived from acoustically derived weights. The system was assessed using a 20-speaker data set of synchronous acoustic and Electromagnetic Articulography (EMA) kinematic data. Results demonstrate that by restricting the reference group to a subset consisting of speakers with strong individual speaker-dependent inversion performance, the PRSW method is able to attain kinematic-independent acoustic-to-articulatory inversion performance nearly matching that of the speaker-dependent model, with an average correlation of 0.62 versus 0.63. This indicates that given a sufficiently complete and appropriately selected reference speaker set for adaptation, it is possible to create effective articulatory models without kinematic training data

Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

The effects of larynx height on vowel production are mitigated by the active control of articulators

The influence of larynx position on vowel articulation is an important topic in understanding speech production, the present-day distribution of linguistic diversity and the evolution of speech and language in our lineage. We introduce here a realistic computer model of the vocal tract, constructed from actual human MRI data, which can learn, using machine learning techniques, to control the articulators in such a way as to produce speech sounds matching as closely as possible to a given set of target vowels. We systematically control the vertical position of the larynx and we quantify the differences between the target and produced vowels for each such position across multiple replications. We report that, indeed, larynx height does affect the accuracy of reproducing the target vowels and the distinctness of the produced vowel system, that there is a “sweet spot” of larynx positions that are optimal for vowel production, but that nevertheless, even extreme larynx positions do not result in a collapsed or heavily distorted vowel space that would make speech unintelligible. Together with other lines of evidence, our results support the view that the vowel space of human languages is influenced by our larynx position, but that other positions of the larynx may also be fully compatible with speech

Evoc-Learn - High quality simulation of early vocal learning

Evoc-Learn is a system for simulating early vocal learning of spoken language in ways that can overcome some of the major bottlenecks in vocal learning. The system consists of VocalTractLab, a geometrical three-dimensional vocal tract model for simulating aeroacoustics and articulatory dynamics, a coarticulation model for controlling the temporal dynamics of articulation, and a sensory feedback system for guiding the learning process. We will demonstrate each component of Evoc-Learn and show how they work together to simulate the learning of highly intelligible speech

Speaker Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the determination of articulatory parameters from acoustic signals, is a difficult but important problem for many speech processing applications, such as automatic speech recognition (ASR) and computer aided pronunciation training (CAPT). In recent years, several approaches have been successfully implemented for speaker dependent models with parallel acoustic and kinematic training data. However, in many practical applications inversion is needed for new speakers for whom no articulatory data is available. In order to address this problem, this dissertation introduces a novel speaker adaptation approach called Parallel Reference Speaker Weighting (PRSW), based on parallel acoustic and articulatory Hidden Markov Models (HMM). This approach uses a robust normalized articulatory space and palate referenced articulatory features combined with speaker-weighted adaptation to form an inversion mapping for new speakers that can accurately estimate articulatory trajectories. The proposed PRSW method is evaluated on the newly collected Marquette electromagnetic articulography - Mandarin Accented English (EMA-MAE) corpus using 20 native English speakers. Cross-speaker inversion results show that given a good selection of reference speakers with consistent acoustic and articulatory patterns, the PRSW approach gives good speaker independent inversion performance even without kinematic training data

Exploration strategies for articulatory synthesis of complex syllable onsets

High-quality articulatory speech synthesis has many potential applications in speech science and technology. However, developing appropriate mappings from linguistic specification to articulatory gestures is difficult and time consuming. In this paper we construct an optimisation-based framework as a first step towards learning these mappings without manual intervention. We demonstrate the production of CCV syllables and discuss the quality of the articulatory gestures with reference to coarticulation

Control concepts for articulatory speech synthesis

We present two concepts for the generation of gestural scores to control an articulatory speech synthesizer. Gestural scores are the common input to the synthesizer and constitute an or- ganized pattern of articulatory gestures. The first concept gen- erates the gestures for an utterance using the phonetic transcrip- tions, phone durations, and intonation commands predicted by the Bonn Open Synthesis System (BOSS) from an arbitrary in- put text. This concept extends the synthesizerto a text-to-speech synthesis system. The idea of the second concept is to use tim- ing informationextracted from ElectromagneticArticulography signals to generate the articulatory gestures. Therefore, it is a concept for the re-synthesis of natural utterances. Finally, ap- plication prospects for the presented synthesizer are discussed

Detection of major ASL sign types in continuous signing for ASL recognition

In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker