Setup for Acoustic-Visual Speech Synthesis by Concatenating Bimodal Units

International audienceThis paper presents preliminary work on building a system able to synthesize concurrently the speech signal and a 3D animation of the speaker's face. This is done by concatenating bimodal diphone units, that is, units that comprise both acoustic and visual information. The latter is acquired using a stereovision technique. The proposed method addresses the problems of asyn- chrony and incoherence inherent in classic approaches to au- diovisual synthesis. Unit selection is based on classic target and join costs from acoustic-only synthesis, which are augmented with a visual join cost. Preliminary results indicate the benefits of the approach, since both the synthesized speech signal and the face animation are of good quality. Planned improvements and enhancements to the system are outlined

Towards a True Acoustic-Visual Speech Synthesis

International audienceThis paper presents an initial bimodal acoustic-visual synthesis system able to generate concurrently the speech signal and a 3D animation of the speaker's face. This is done by concatenating bimodal diphone units that consist of both acoustic and visual information. The latter is acquired using a stereovision technique. The proposed method addresses the problems of asyn- chrony and incoherence inherent in classic approaches to audiovisual synthesis. Unit selection is based on classic target and join costs from acoustic-only synthesis, which are augmented with a visual join cost. Preliminary results indicate the benefits of this approach, since both the synthesized speech signal and the face animation are of good quality

Predicting Tongue Positions from Acoustics and Facial Features

International audienceWe test the hypothesis that adding information regarding the positions of electromagnetic articulograph (EMA) sensors on the lips and jaw can improve the results of a typical acoustic-to-EMA mapping system, based on support vector regression, that targets the tongue sensors. Our initial motivation is to use such a system in the context of adding a tongue animation to a talking head built on the basis of concatenating bimodal acoustic-visual units. For completeness, we also train a system that maps only jaw and lip information to tongue information

Some consideration on expressive audiovisual speech corpus acquisition using a multimodal platform

International audienceIn this paper, we present a multimodal acquisition setup that combines different motion-capture systems. This system is mainly aimed for recording expressive audiovisual corpus in the context of audiovisual speech synthesis. When dealing with speech recording, the standard optical motion-capture systems fail in tracking the articulators finely, especially the inner mouth region, due to the disappearing of certain markers during the articulation. Also, some systems have limited frame rates and are not suitable for smooth speech tracking. In this work, we demonstrate how those limitations can be overcome by creating a heterogeneous system taking advantage of different tracking systems. In the scope of this work, we recorded a prototypical corpus using our combined system for a single subject. This corpus was used to validate our multimodal data acquisition protocol and to assess the quality of the expressiveness before recording a large corpus. We conducted two evaluations of the recorded data, the first one concerns the production aspect of speech and the second one focuses on the speech perception aspect (both evaluations concern visual and acoustic modalities). Production analysis allowed us to identify characteristics specific to each expressive context. This analysis showed that the expressive content of the recorded data is globally in line with what is commonly expected in the literature. The perceptual evaluation, conducted as a human emotion recognition task using different types of stimulus, confirmed that the different recorded emotions were well perceived

Automatic Video Self Modeling for Voice Disorder

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with fluency disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice disorders. Starting with a video recording of a voice-disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient’s original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Results of both objective measurements and subjective evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques

Adaptive Fusion Techniques for Effective Multimodal Deep Learning

Effective fusion of data from multiple modalities, such as video, speech, and text, is a challenging task due to the heterogeneous nature of multimodal data. In this work, we propose fusion techniques that aim to model context from different modalities effectively. Instead of defining a deterministic fusion operation, such as concatenation, for the network, we let the network decide “how” to combine given multimodal features more effectively. We propose two networks: 1) Auto-Fusion network, which aims to compress information from different modalities while preserving the context, and 2) GAN-Fusion, which regularizes the learned latent space given context from complementing modalities. A quantitative evaluation on the tasks of multimodal machine translation and emotion recognition suggests that our adaptive networks can better model context from other modalities than all existing methods, many of which employ massive transformer-based networks

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes