Synthetic Speech Classification: IEEE Signal Processing Cup 2022 challenge

Autor:	Rahmun, Mahieyin, Khan, Rafat Hasan, Aurpa, Tanjim Taharat, Khan, Sadia, Nahiyan, Zulker Nayeen, Almas, Mir Sayad Bin, Rajib, Rakibul Hasan, Hassan, Syeda Sakira
Rok vydání:	2024
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	The aim of this project is to implement and design arobust synthetic speech classifier for the IEEE Signal ProcessingCup 2022 challenge. Here, we learn a synthetic speech attributionmodel using the speech generated from various text-to-speech(TTS) algorithms as well as unknown TTS algorithms. Weexperiment with both the classical machine learning methodssuch as support vector machine, Gaussian mixture model, anddeep learning based methods such as ResNet, VGG16, and twoshallow end-to-end networks. We observe that deep learningbased methods with raw data demonstrate the best performance.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.13279 Zobrazit plný text záznamu View this record from Arxiv