Mage - Reactive articulatory feature control of HMM-based parametric speech synthesis

In this paper, we present the integration of articulatory control into MAGE, a framework for realtime and interactive (reactive) parametric speech synthesis using hidden Markov models (HMMs). MAGE is based on the speech synthesis engine from HTS and uses acoustic features (spectrum and f0) to model and synthesize speech. In this work, we replace the standard acoustic models with models combining acoustic and articulatory features, such as tongue, lips and jaw positions. We then use feature-space-switched articulatory-to-acoustic regression matrices to enable us to control the spectral acoustic features by manipulating the articulatory features. Combining this synthesis model with MAGE allows us to interactively and intuitively modify phones synthesized in real time, for example transforming one phone into another, by controlling the configuration of the articulators in a visual display. Index Terms: speech synthesis, reactive, articulators 1

Speech Synthesis from Text and Ultrasound Tongue Image-based Articulatory Input

Articulatory information has been shown to be effective in improving the performance of HMM-based and DNN-based text-to-speech synthesis. Speech synthesis research focuses traditionally on text-to-speech conversion, when the input is text or an estimated linguistic representation, and the target is synthesized speech. However, a research field that has risen in the last decade is articulation-to-speech synthesis (with a target application of a Silent Speech Interface, SSI), when the goal is to synthesize speech from some representation of the movement of the articulatory organs. In this paper, we extend traditional (vocoder-based) DNN-TTS with articulatory input, estimated from ultrasound tongue images. We compare text-only, ultrasound-only, and combined inputs. Using data from eight speakers, we show that that the combined text and articulatory input can have advantages in limited-data scenarios, namely, it may increase the naturalness of synthesized speech compared to single text input. Besides, we analyze the ultrasound tongue recordings of several speakers, and show that misalignments in the ultrasound transducer positioning can have a negative effect on the final synthesis performance.Comment: accepted at SSW11 (11th Speech Synthesis Workshop

Reactive Statistical Mapping: Towards the Sketching of Performative Control with Data

Part 1: Fundamental IssuesInternational audienceThis paper presents the results of our participation to the ninth eNTERFACE workshop on multimodal user interfaces. Our target for this workshop was to bring some technologies currently used in speech recognition and synthesis to a new level, i.e. being the core of a new HMM-based mapping system. The idea of statistical mapping has been investigated, more precisely how to use Gaussian Mixture Models and Hidden Markov Models for realtime and reactive generation of new trajectories from inputted labels and for realtime regression in a continuous-to-continuous use case. As a result, we have developed several proofs of concept, including an incremental speech synthesiser, a software for exploring stylistic spaces for gait and facial motion in realtime, a reactive audiovisual laughter and a prototype demonstrating the realtime reconstruction of lower body gait motion strictly from upper body motion, with conservation of the stylistic properties. This project has been the opportunity to formalise HMM-based mapping, integrate various of these innovations into the Mage library and explore the development of a realtime gesture recognition tool

Context-aware speech synthesis: A human-inspired model for monitoring and adapting synthetic speech

The aim of this PhD thesis is to illustrate the development a computational model for speech synthesis, which mimics the behaviour of human speaker when they adapt their production to their communicative conditions. The PhD project was motivated by the observed differences between state-of-the- art synthesiser’s speech and human production. In particular, synthesiser outcome does not exhibit any adaptation to communicative context such as environmental disturbances, listener’s needs, or speech content meanings, as the human speech does. No evaluation is performed by standard synthesisers to check whether their production is suitable for the communication requirements. Inspired by Lindblom's Hyper and Hypo articulation theory (H&H) theory of speech production, the computational model of Hyper and Hypo articulation theory (C2H) is proposed. This novel computational model for automatic speech production is designed to monitor its outcome and to be able to control the effort involved in the synthetic speech generation. Speech transformations are based on the hypothesis that low-effort attractors for a human speech production system can be identified. Such acoustic configurations are close to minimum possible effort that a speaker can make in speech production. The interpolation/extrapolation along the key dimension of hypo/hyper-articulation can be motivated by energetic considerations of phonetic contrast. The complete reactive speech synthesis is enabled by adding a negative perception feedback loop to the speech production chain in order to constantly assess the communicative effectiveness of the proposed adaptation. The distance to the original communicative intents is the control signal that drives the speech transformations. A hidden Markov model (HMM)-based speech synthesiser along with the continuous adaptation of its statistical models is used to implement the C2H model. A standard version of the synthesis software does not allow for transformations of speech during the parameter generation. Therefore, the generation algorithm of one the most well-known speech synthesis frameworks, HMM/DNN-based speech synthesis framework (HTS), is modified. The short-time implementation of speech intelligibility index (SII), named extended speech intelligibility index (eSII), is also chosen as the main perception measure in the feedback loop to control the transformation. The effectiveness of the proposed model is tested by performing acoustic analysis, objective, and subjective evaluations. A key assessment is to measure the control of the speech clarity in noisy condition, and the similarities between the emerging modifications and human behaviour. Two objective scoring methods are used to assess the speech intelligibility of the implemented system: the speech intelligibility index (SII) and the index based upon the Dau measure (Dau). Results indicate that the intelligibility of C2H-generated speech can be continuously controlled. The effectiveness of reactive speech synthesis and of the phonetic contrast motivated transforms is confirmed by the acoustic and objective results. More precisely, in the maximum-strength hyper-articulation transformations, the improvement with respect to non-adapted speech is above 10% for all intelligibility indices and tested noise conditions

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