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

Towards Multi-Modal Interactions in Virtual Environments: A Case Study

We present research on visualization and interaction in a realistic model of an existing theatre. This existing ‘Muziek¬centrum’ offers its visitors information about performances by means of a yearly brochure. In addition, it is possible to get information at an information desk in the theatre (during office hours), to get information by phone (by talking to a human or by using IVR). The database of the theater holds the information that is available at the beginning of the ‘theatre season’. Our aim is to make this information more accessible by using multi-modal accessible multi-media web pages. A more general aim is to do research in the area of web-based services, in particu¬lar interactions in virtual environments

Aspects of Speaking-Face Data Corpus Design Methodology

This paper develops a methodology for the design of audiovideo data corpora of the speaking face. Existing corpora are surveyed and the principles of data specification, data description and statistical representation are analysed both from an application-driven and from a scientifically motivated perspective. Furthermore, the possibility of "opportunistic" design of speaking-face data corpora is considered

DeepVoCoder: A CNN model for compression and coding of narrow band speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.Web of Science7750897508

Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

On the Development of Adaptive and User-Centred Interactive Multimodal Interfaces

Multimodal systems have attained increased attention in recent years, which has made possible important improvements in the technologies for recognition, processing, and generation of multimodal information. However, there are still many issues related to multimodality which are not clear, for example, the principles that make it possible to resemble human-human multimodal communication. This chapter focuses on some of the most important challenges that researchers have recently envisioned for future multimodal interfaces. It also describes current efforts to develop intelligent, adaptive, proactive, portable and affective multimodal interfaces

Review of Research on Speech Technology: Main Contributions From Spanish Research Groups

In the last two decades, there has been an important increase in research on speech technology in Spain, mainly due to a higher level of funding from European, Spanish and local institutions and also due to a growing interest in these technologies for developing new services and applications. This paper provides a review of the main areas of speech technology addressed by research groups in Spain, their main contributions in the recent years and the main focus of interest these days. This description is classified in five main areas: audio processing including speech, speaker characterization, speech and language processing, text to speech conversion and spoken language applications. This paper also introduces the Spanish Network of Speech Technologies (RTTH. Red Temática en Tecnologías del Habla) as the research network that includes almost all the researchers working in this area, presenting some figures, its objectives and its main activities developed in the last years

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output