This work focuses on improving the Spoken Language Identification (LangId) system for a challenge that focuses on developing robust language identification systems that are reliable for non-standard, accented (Singaporean accent), spontaneous code-switched, and child-directed speech collected via Zoom.We propose a two-stage Encoder-Decoder-based E2E model.The encoder module consists of 1D depth-wise separable convolutions with Squeeze-and-Excitation (SE) layers with a global context.The decoder module uses an attentive temporal pooling mechanism to get fixed length time-independent feature representation.The total number of parameters in the model is around 22.1 M, which is relatively light compared to using some large-scale pre-trained speech models.We achieved an EER of 15.6% in the closed track and 11.1% in the open track (baseline system 22.1%).We also curated additional LangId data from YouTube videos (having Singaporean speakers), which will be released for public use.

Deep Learning has become interestingly popular in the field of computer vision, mostly attaining near or above human-level performance in various vision tasks. But recent work has also demonstrated that these deep neural networks are very vulnerable to adversarial examples (adversarial examples - inputs to a model which are naturally similar to original data but fools the model in classifying it into a wrong class). In this work, we proposed a novel method to train deep learning models on an image classification task. We used a reward-based optimization function, similar to the vanilla policy gradient method in reinforcement learning to train our model instead of conventional cross-entropy loss. An empirical evaluation on cifar10 dataset showed that our method outperforms the same model architecture trained using cross-entropy loss function (on adversarial training). At the same time, our method generalizes better to the training data with the difference in test accuracy and train accuracy < 2% for most of the time as compared to cross-entropy one, whose difference most of the time remains > 2%.

Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys at home or a friend in a crowd. An intriguing property of some classical search tasks is an asymmetry such that finding a target A among distractors B can be easier than finding B among A. To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and a search image as inputs and produces a sequence of eye movements until the target is found. The model integrates eccentricity-dependent visual recognition with target-dependent top-down cues. We compared the model against human behavior in six paradigmatic search tasks that show asymmetry in humans. Without prior exposure to the stimuli or task-specific training, the model provides a plausible mechanism for search asymmetry. We hypothesized that the polarity of search asymmetry arises from experience with the natural environment. We tested this hypothesis by training the model on augmented versions of ImageNet where the biases of natural images were either removed or reversed. The polarity of search asymmetry disappeared or was altered depending on the training protocol. This study highlights how classical perceptual properties can emerge in neural network models, without the need for task-specific training, but rather as a consequence of the statistical properties of the developmental diet fed to the model. All source code and data are publicly available at https://github.com/kreimanlab/VisualSearchAsymmetry.