Continuous Interaction with a Virtual Human

Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access

Human factors issues associated with the use of speech technology in the cockpit

The human factors issues associated with the use of voice technology in the cockpit are summarized. The formulation of the LHX avionics suite is described and the allocation of tasks to voice in the cockpit is discussed. State-of-the-art speech recognition technology is reviewed. Finally, a questionnaire designed to tap pilot opinions concerning the allocation of tasks to voice input and output in the cockpit is presented. This questionnaire was designed to be administered to operational AH-1G Cobra gunship pilots. Half of the questionnaire deals specifically with the AH-1G cockpit and the types of tasks pilots would like to have performed by voice in this existing rotorcraft. The remaining portion of the questionnaire deals with an undefined rotorcraft of the future and is aimed at determining what types of tasks these pilots would like to have performed by voice technology if anything was possible, i.e. if there were no technological constraints

Application of singing synthesis techniquest to bertsolaritza

237 p

Fast Speech in Unit Selection Speech Synthesis

Moers-Prinz D. Fast Speech in Unit Selection Speech Synthesis. Bielefeld: Universität Bielefeld; 2020.Speech synthesis is part of the everyday life of many people with severe visual disabilities. For those who are reliant on assistive speech technology the possibility to choose a fast speaking rate is reported to be essential. But also expressive speech synthesis and other spoken language interfaces may require an integration of fast speech. Architectures like formant or diphone synthesis are able to produce synthetic speech at fast speech rates, but the generated speech does not sound very natural. Unit selection synthesis systems, however, are capable of delivering more natural output. Nevertheless, fast speech has not been adequately implemented into such systems to date. Thus, the goal of the work presented here was to determine an optimal strategy for modeling fast speech in unit selection speech synthesis to provide potential users with a more natural sounding alternative for fast speech output

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

Concatenative speech synthesis: a Framework for Reducing Perceived Distortion when using the TD-PSOLA Algorithm

This thesis presents the design and evaluation of an approach to concatenative speech synthesis using the Titne-Domain Pitch-Synchronous OverLap-Add (I'D-PSOLA) signal processing algorithm. Concatenative synthesis systems make use of pre-recorded speech segments stored in a speech corpus. At synthesis time, the `best' segments available to synthesise the new utterances are chosen from the corpus using a process known as unit selection. During the synthesis process, the pitch and duration of these segments may be modified to generate the desired prosody. The TD-PSOLA algorithm provides an efficient and essentially successful solution to perform these modifications, although some perceptible distortion, in the form of `buzzyness', may be introduced into the speech signal. Despite the popularity of the TD-PSOLA algorithm, little formal research has been undertaken to address this recognised problem of distortion. The approach in the thesis has been developed towards reducing the perceived distortion that is introduced when TD-PSOLA is applied to speech. To investigate the occurrence of this distortion, a psychoacoustic evaluation of the effect of pitch modification using the TD-PSOLA algorithm is presented. Subjective experiments in the form of a set of listening tests were undertaken using word-level stimuli that had been manipulated using TD-PSOLA. The data collected from these experiments were analysed for patterns of co- occurrence or correlations to investigate where this distortion may occur. From this, parameters were identified which may have contributed to increased distortion. These parameters were concerned with the relationship between the spectral content of individual phonemes, the extent of pitch manipulation, and aspects of the original recordings. Based on these results, a framework was designed for use in conjunction with TD-PSOLA to minimise the possible causes of distortion. The framework consisted of a novel speech corpus design, a signal processing distortion measure, and a selection process for especially problematic phonemes. Rather than phonetically balanced, the corpus is balanced to the needs of the signal processing algorithm, containing more of the adversely affected phonemes. The aim is to reduce the potential extent of pitch modification of such segments, and hence produce synthetic speech with less perceptible distortion. The signal processingdistortion measure was developed to allow the prediction of perceptible distortion in pitch-modified speech. Different weightings were estimated for individual phonemes,trained using the experimental data collected during the listening tests.The potential benefit of such a measure for existing unit selection processes in a corpus-based system using TD-PSOLA is illustrated. Finally, the special-case selection process was developed for highly problematic voiced fricative phonemes to minimise the occurrence of perceived distortion in these segments. The success of the framework, in terms of generating synthetic speech with reduced distortion, was evaluated. A listening test showed that the TD-PSOLA balanced speech corpus may be capable of generating pitch-modified synthetic sentences with significantly less distortion than those generated using a typical phonetically balanced corpus. The voiced fricative selection process was also shown to produce pitch-modified versions of these phonemes with less perceived distortion than a standard selection process. The listening test then indicated that the signal processing distortion measure was able to predict the resulting amount of distortion at the sentence-level after the application of TD-PSOLA, suggesting that it may be beneficial to include such a measure in existing unit selection processes. The framework was found to be capable of producing speech with reduced perceptible distortion in certain situations, although the effects seen at the sentence-level were less than those seen in the previous investigative experiments that made use of word-level stimuli. This suggeststhat the effect of the TD-PSOLA algorithm cannot always be easily anticipated due to the highly dynamic nature of speech, and that the reduction of perceptible distortion in TD-PSOLA-modified speech remains a challenge to the speech community

Application of singing synthesis techniquest to bertsolaritza

237 p

Automatic Content Generation for Video Self Modeling

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. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. Voice conversion is then applied to match the new speech to the original voice. Time markers extracted from the original and new speech track are used to re-sample the video track for lip synchronization. We use an adaptive re-sampling strategy to minimize motion jitter, and apply bilinear and optical-flow based interpolation to ensure the image quality. Both objective measurements and subjective evaluations demonstrate the effectiveness of the proposed techniques

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