In this study, we have presented a deep learning-based implementation for speech emotion recognition (SER). The system combines a deep convolutional neural network (DCNN) and a bidirectional long-short term memory (BLSTM) network with a time-distributed flatten (TDF) layer. The proposed model has been applied for the recently built audio-only Bangla emotional speech corpus SUBESCO. A series of experiments were carried out to analyze all the models discussed in this paper for baseline, cross-lingual, and multilingual training-testing setups. The experimental results reveal that the model with a TDF layer achieves better performance compared with other state-of-the-art CNN-based SER models which can work on both temporal and sequential representation of emotions. For the cross-lingual experiments, cross-corpus training, multi-corpus training, and transfer learning were employed for the Bangla and English languages using the SUBESCO and RAVDESS datasets. The proposed model has attained a state-of-the-art perceptual efficiency achieving weighted accuracies (WAs) of 86.9%, and 82.7% for the SUBESCO and RAVDESS datasets, respectively.

SUBESCO is an audio-only emotional speech corpus for Bangla language. The total duration of the corpus is in excess of 7 hours containing 7000 utterances, and it is the largest emotional speech corpus available for this language. Twenty native speakers participated in the gender-balanced set, each recording of 10 sentences simulating seven targeted emotions. Fifty university students participated in the evaluation of this corpus. Each audio clip of this corpus, except those of Disgust emotion, was validated four times by male and female raters. Raw hit rates and unbiased rates were calculated producing scores above chance level of responses. Overall recognition rate was reported to be above 70% for human perception tests. Kappa statistics and intra-class correlation coefficient scores indicated high-level of inter-rater reliability and consistency of this corpus evaluation. SUBESCO is an Open Access database, licensed under Creative Common Attribution 4.0 International, and can be downloaded free of charge from the web link: https://doi.org/10.5281/zenodo.4526477.

Islam. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license.

Head motion during Computed Tomographic (CT) brain imaging studies can adversely affect the reconstructed image through distortion, loss of resolution and other related artifacts. In this paper, we propose a marker based innovative approach to detect and mitigate motion artifacts in three dimensional cone-beam brain CT systems without using any external motion tracking sensor. Motion is detected using correlations between the adjacent projections. Once motion is detected, motion parameters (i.e. six degrees-of-freedom of motions) are estimated using a numerical optimization technique. Artifacts, caused by motions, are mitigated by using a modified form Feldkemp-Davis-Kress (FDK) algorithm which uses the estimated motion parameters in back-projection stage. The proposed approach has been evaluated on a modified three-dimensional Shepp-Logan phantom with a range of simulated motions. Simulation results demonstrate a quantitative and qualitative validation of motion detection and artifacts mitigation technique.

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