To evaluate the proposed audio-to-visual conversion algorithm in a more complex scenario, the second database, consisting of videos of a person pronouncing a set of 120 phonetically balanced sentences (uttered 3 times), was employed.
To address this limitation, Choi et al. [15] have proposed a hidden Markov model inversion (HMMI) method for audio-visual conversion, which was originally introduced in [30] in the context of robust speech recognition.
This is really helpful because we always get the random variation after we calculate the residue between the real input visual parameters and the outputs from audio-visual conversion models.
The idea of HMMI for audio-to-visual conversion is to estimate the visual features based on the trained AV-HMM, in such a way that the probability that the whole audio-visual observation has been generated by the model is maximized, that is Õ v = arg max { O v P ( O a, O v | λ av ) }, (2).
A fair comparison, in terms of AMSE and ACC, between the proposed audio-to-visual conversion algorithm and previous research is not an easy task since there is neither a common audio-visual corpus for evaluation, nor a common quality metric [15, 17, 29, 31, 44, 45].
In audio-input conversion, the improvement ratio of vowels is better than that of consonants; however, in audio-visual conversion, the improvement ratio of consonants is better because image features compensate for the conversion of the consonant part, which is degraded by the noisy signal in audio-input conversion.
To evaluate the influence of the structure of the covariance matrices on the audio-to-visual conversion algorithm, experiments were performed using AV-HMMs with diagonal and full covariance matrices, different number of states and mixtures in the ranges from 2 to 19, and from 3 to 20, respectively, and different values of the co-articulation parameter t c in the range from 0 to 7.
Visual acuity conversion for Snellen fraction to ETDRS was required for five patients.
The CCK-8 reagents were added to the each wells at 0 h, 24 h, 48 h, and 72 h post-transfection, and cells were diluted in normal culture medium at 37°C until visual color conversion occurred.
In this paper, we propose a novel multi-level and multi-modal approach aiming at addressing this challenging and practical problem, with the key idea being semantic-aware visual-to-tactile conversion through semantic image categorization and segmentation, and semantic-driven image simplification.
