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Stars in their eyes: What eye-tracking reveal about multimedia perceptual quality
Perceptual multimedia quality is of paramount
importance to the continued take-up and proliferation of multimedia applications: users will not use and pay for applications if they are perceived to be of low quality. Whilst traditionally distributed multimedia quality has been characterised by Quality of Service (QoS) parameters, these neglect the user perspective of the issue of quality. In order to redress this shortcoming, we characterise the user multimedia perspective using the Quality of Perception (QoP) metric, which encompasses not only a userâs satisfaction with the quality of a multimedia presentation, but also his/her ability to analyse,
synthesise and assimilate informational content of multimedia. In recognition of the fact that monitoring eye movements offers insights into visual perception, as well as the associated
attention mechanisms and cognitive processes, this paper reports on the results of a study investigating the impact of differing multimedia presentation frame rates on user QoP and eye path data. Our results show that provision of higher frame rates, usually assumed to provide better multimedia presentation quality, do not significantly impact upon the median coordinate value of eye path data. Moreover, higher frame rates do not significantly increase level of participant information assimilation, although they do significantly improve overall user enjoyment and quality perception of the multimedia content being shown
How visual cues to speech rate influence speech perception
Spoken words are highly variable and therefore listeners interpret speech sounds relative to the surrounding acoustic context, such as the speech rate of a preceding sentence. For instance, a vowel midway between short /É/ and long /a:/ in Dutch is perceived as short /É/ in the context of preceding slow speech, but as long /a:/ if preceded by a fast context. Despite the well-established influence of visual articulatory cues on speech comprehension, it remains unclear whether visual cues to speech rate also influence subsequent spoken word recognition. In two âGo Fishâ-like experiments, participants were presented with audio-only (auditory speech + fixation cross), visual-only (mute videos of talking head), and audiovisual (speech + videos) context sentences, followed by ambiguous target words containing vowels midway between short /É/ and long /a:/. In Experiment 1, target words were always presented auditorily, without visual articulatory cues. Although the audio-only and audiovisual contexts induced a rate effect (i.e., more long /a:/ responses after fast contexts), the visual-only condition did not. When, in Experiment 2, target words were presented audiovisually, rate effects were observed in all three conditions, including visual-only. This suggests that visual cues to speech rate in a context sentence influence the perception of following visual target cues (e.g., duration of lip aperture), which at an audiovisual integration stage bias participantsâ target categorization responses. These findings contribute to a better understanding of how what we see influences what we hear
Analyzing Input and Output Representations for Speech-Driven Gesture Generation
This paper presents a novel framework for automatic speech-driven gesture
generation, applicable to human-agent interaction including both virtual agents
and robots. Specifically, we extend recent deep-learning-based, data-driven
methods for speech-driven gesture generation by incorporating representation
learning. Our model takes speech as input and produces gestures as output, in
the form of a sequence of 3D coordinates. Our approach consists of two steps.
First, we learn a lower-dimensional representation of human motion using a
denoising autoencoder neural network, consisting of a motion encoder MotionE
and a motion decoder MotionD. The learned representation preserves the most
important aspects of the human pose variation while removing less relevant
variation. Second, we train a novel encoder network SpeechE to map from speech
to a corresponding motion representation with reduced dimensionality. At test
time, the speech encoder and the motion decoder networks are combined: SpeechE
predicts motion representations based on a given speech signal and MotionD then
decodes these representations to produce motion sequences. We evaluate
different representation sizes in order to find the most effective
dimensionality for the representation. We also evaluate the effects of using
different speech features as input to the model. We find that mel-frequency
cepstral coefficients (MFCCs), alone or combined with prosodic features,
perform the best. The results of a subsequent user study confirm the benefits
of the representation learning.Comment: Accepted at IVA '19. Shorter version published at AAMAS '19. The code
is available at
https://github.com/GestureGeneration/Speech_driven_gesture_generation_with_autoencode
Prosodic Event Recognition using Convolutional Neural Networks with Context Information
This paper demonstrates the potential of convolutional neural networks (CNN)
for detecting and classifying prosodic events on words, specifically pitch
accents and phrase boundary tones, from frame-based acoustic features. Typical
approaches use not only feature representations of the word in question but
also its surrounding context. We show that adding position features indicating
the current word benefits the CNN. In addition, this paper discusses the
generalization from a speaker-dependent modelling approach to a
speaker-independent setup. The proposed method is simple and efficient and
yields strong results not only in speaker-dependent but also
speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur
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