Generative Artificial Intelligence and GPT using Deep Learning: A Comprehensive Vision, Applications Trends and Challenges

Generative Artificial intelligence is a prominent and recently emerging subdomain in the field of artificial intelligence. It deals with question-answering based on natural language processing. This paper discusses recent methodologies adopted by researchers in this field. It also discusses GAI and machine learning techniques for multimodal applications like image, text and audio-based data generation. This meta-analysis and survey was done from prominent research up to 2023 from the Scopus Database consisting of reputed and authenticated research papers.The research contribution is twofold 1. To analyze the recent research and applications at the industry level 2. To identify techniques and associated limitations. This would further aid practitioners  to address future challenges

Lombard speech synthesis using transfer learning in a Tacotron text-to-speech system

Currently, there is increasing interest to use sequence-to-sequence models in text-to-speech (TTS) synthesis with attention like that in Tacotron models. These models are end-to-end, meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech of good quality. However, in challenging speaking styles, such as Lombard speech, it is difficult to record sufficiently large speech corpora. Therefore, we propose a transfer learning method to adapt a TTS system of normal speaking style to Lombard style. We also experiment with a WaveNet vocoder along with a traditional vocoder (WORLD) in the synthesis of Lombard speech. The subjective and objective evaluation results indicated that the proposed adaptation system coupled with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in the synthesis of Lombard speechPeer reviewe