Zero-Shot Speech-to-Speech Summarization using Unsupervised Learning and Generative Models

Parallel dataset which needs large number of aligned sources and target language pairings have traditionally been used to develop speech to speech translation system. This study presents a new method for Zero short speech to Speech translation (ZSSST) that relies on generative methods and other unsupervised learning methods. The main concept is to train the methods that can directly translate spoken language from one language to another without using parallel corpora. This will be achieved by utilizing big, unlabeled multilingual voice datasets. The suggested method learns to translate spoken input from one language into another by using unsupervised learning techniques like generative adversarial network (GAN) and Variation autoencoder. To enable speech to speech translation without direct supervision, this method is trained to produced speech in target language. This technology outperforms the limitations of conventional approaches and may operate with numerous languages concurrently, allowing for cross lingual transmission. Because it doesn't necessitate domain specific data or parallel voice corpora, this method ensures scalability for languages with limited resources. International diplomacy, cross border trade and real time personal communication are just a few examples of the many various applications that could benefit from the proposed method's improved accessibility, adaptability and efficiency in multilingual communication. Experimental results demonstrate the efficiency of this method on multilingual datasets and the metrics of existing methods with proposed methods are compared. By fixing the scalability problem and opening the path for more generic system that can easily handle several languages without expensive and labor-intensive parallel datasets, this method has major consequences for the future of real time voice translation.