ITTS model: speech generation for image captioning using feature extraction for end-to-end synthesis
by Tushar H. Ghorpade; Subhash K. Shinde
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 21, No. 2, 2023

Abstract: The current growth in e-content is attributed to, information exchanged through social media, e-news, etc. Several researchers have proposed an encoder-decoder model with impressive accuracy. This paper exploits feature extraction from images and text for the encoder model using a word embedding method with proposed convolutional layers. State-of-the-art image-to-text and text-to-speech (ITTS) systems learn models separately, one describes the content of an image and the other follows with speech generation. We adopted the Tacotron model for the naturalness of a text with most popular datasets. It can also consistently analyse using evaluation metrics like bilingual evaluation understudy (BLEU), METOr, and mean opinion scale (MOS). The proposed method can significantly enhance the performance and competitive results of a standard image caption and speech generation model. The results show that we obtained an improvement by almost 4% to 5% BLEU score in image captioning model and approximately MOS is 3.73 in speech model.

Online publication date: Mon, 19-Jun-2023

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