Identity, accent, style, and emotions are essential components of human speech. Voice conversion (VC) techniques process the speech signals of two input speakers and other modalities of auxiliary information such as prompts and emotion tags. It changes para-linguistic features from one to another, while maintaining linguistic contents. Recently, VC models have made rapid advancements in both generation quality and personalization capabilities. These developments have attracted considerable attention for diverse applications, including privacy preservation, voice-print reproduction for the deceased, and dysarthric speech recovery. However, these models only learn non-robust features due to the clean training data. Subsequently, it results in unsatisfactory performances when dealing with degraded input speech in real-world scenarios, including additional noise, reverberation, adversarial attacks, or even minor perturbation. Hence, it demands robust deployments, especially in real-world settings. Although latest researches attempt to find potential attacks and countermeasures for VC systems, there remains a significant gap in the comprehensive understanding of how robust the VC model is under input manipulation. here also raises many questions: For instance, to what extent do different forms of input degradation attacks alter the expected output of VC models? Is there potential for optimizing these attack and defense strategies? To answer these questions, we classify existing attack and defense methods from the perspective of input manipulation and evaluate the impact of degraded input speech across four dimensions, including intelligibility, naturalness, timbre similarity, and subjective perception. Finally, we outline open issues and future directions.

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.

This paper describes the IUST NLP Lab submission to the Prompting Large Language Models as Explainable Metrics Shared Task at the Eval4NLP 2023 Workshop on Evaluation & Comparison of NLP Systems. We have proposed a zero-shot prompt-based strategy for explainable evaluation of the summarization task using Large Language Models (LLMs). The conducted experiments demonstrate the promising potential of LLMs as evaluation metrics in Natural Language Processing (NLP), particularly in the field of summarization. Both few-shot and zero-shot approaches are employed in these experiments. The performance of our best provided prompts achieved a Kendall correlation of 0.477 with human evaluations in the text summarization task on the test data. Code and results are publicly available on GitHub.

In recent years, deep learning has achieved remarkable success in various fields such as image recognition, natural language processing, and speech recognition. The effectiveness of deep learning largely depends on the optimization methods used to train deep neural networks. In this paper, we provide an overview of first-order optimization methods such as Stochastic Gradient Descent, Adagrad, Adadelta, and RMSprop, as well as recent momentum-based and adaptive gradient methods such as Nesterov accelerated gradient, Adam, Nadam, AdaMax, and AMSGrad. We also discuss the challenges associated with optimization in deep learning and explore techniques for addressing these challenges, including weight initialization, batch normalization, and layer normalization. Finally, we provide recommendations for selecting optimization methods for different deep learning tasks and datasets. This paper serves as a comprehensive guide to optimization methods in deep learning and can be used as a reference for researchers and practitioners in the field.

FPN (Feature Pyramid Network) is a type of convolutional neural network architecture for object detection tasks. It is designed to improve the performance of object detection models by making use of both high-level and low-level features from the input image. The basic idea behind FPN is to build a pyramid of features, where each level in the pyramid represents a different scale or resolution of the input image. The top of the pyramid represents the high-level, semantically rich features, while the bottom of the pyramid represents the low-level, fine-grained features. By combining features from different levels in the pyramid, the model is able to make use of both the semantically rich high-level features and the fine-grained low-level features to improve the accuracy of object detection.

A critical phase of machine learning is feature selection. To enhance the model's effectiveness and generalizability, it includes choosing and creating a subset of features from a dataset that are most related to the target variable. There are many different techniques that can be used for feature selection, and the appropriate technique will depend on the specific problem and the type of data you are working with. It's important to note that feature selection is a trade-off between model complexity and predictive power. Adding more features to a model can potentially improve its performance, but it can also make the model more difficult to interpret and increase the risk of overfitting (performing well on the training data but poorly on unseen data). A hybrid method involves combining two or more of the above methods to find the optimal subset of features.

The global Machine Learning Market research report is a compilation of the detailed study of each and every aspect related to the Machine Learning industry. The research report offers a thorough analysis of all the Market related data supported by reliable numerical data. The research report holds the crucial data regarding the Valuation of the Machine Learning industry in the past years. It also includes a prediction for numerical data regarding the future Market size and volume. The detailed study on the CAGR at which the Machine Learning Market is anticipated to expand in the future is provided in the study.

This article reviews meta-learning also known as learning-to-learn which seeks rapid and accurate model adaptation to unseen tasks with applications in highly automated AI, few-shot learning, natural language processing and robotics. Unlike deep learning, meta-learning can be applied to few-shot high-dimensional datasets and considers further improving model generalization to unseen tasks. Deep learning is focused upon in-sample prediction and meta-learning concerns model adaptation for out-of-sample prediction. Meta-learning can continually perform self-improvement to achieve highly autonomous AI. Meta-learning may serve as an additional generalization block complementary for original deep learning model. Meta-learning seeks adaptation of machine learning models to unseen tasks which are vastly different from trained tasks. Meta-learning with coevolution between agent and environment provides solutions for complex tasks unsolvable by training from scratch. Meta-learning methodology covers a wide range of great minds and thoughts. We briefly introduce meta-learning methodologies in the following categories: black-box meta-learning, metric-based meta-learning, layered meta-learning and Bayesian meta-learning framework. Recent applications concentrate upon the integration of meta-learning with other machine learning framework to provide feasible integrated problem solutions. We briefly present recent meta-learning advances and discuss potential future research directions.

The Artificial Intelligence (AI) In Fintech Market report predicts promising growth and development during the period 2020-2027. The Artificial Intelligence (AI) In Fintech Market survey report represents vital statistical data represented in an organized format such as graphs, charts, tables, and figures to provide a detailed understanding of the Artificial Intelligence (AI) In Fintech Market in a simple manner. The report covers an in-depth analysis of the Artificial Intelligence (AI) In Fintech market and offers key insights on current and emerging trends, market drivers, and market insights offered by industry experts. The report examines the impact of COVID-19 on market growth. The study provides comprehensive coverage of the impact of the COVID-19 pandemic on the Artificial Intelligence (AI) In Fintech market and its key segments.

Deep learning has continued its forward movement during 2019 with advances in many exciting research areas like generative adversarial networks (GANs), auto-encoders, and reinforcement learning. In terms of deployments, deep learning is the darling of many contemporary application areas such as computer vision, image recognition, speech recognition, natural language processing, machine translation, autonomous vehicles, and many more. Earlier this year, we saw Google AI Language revolutionize the NLP segment of deep learning with the new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. The already seminal paper was released on arXiv on May 24. This has led to a storm of follow-on research results.