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Whispered speech is a special way of pronunciation without using vocal cordvibration. A whispered speech does not contain a fundamental frequency, and itsenergy is about 20dB lower than that of a normal speech. Converting a whisperedspeech into a normal speech can improve speech quality and intelligibility. Inthis paper, a novel attention-guided generative adversarial network modelincorporating an autoencoder, a Siamese neural network, and an identity mappingloss function for whisper to normal speech conversion (AGAN-W2SC) is proposed.The proposed method avoids the challenge of estimating the fundamentalfrequency of the normal voiced speech converted from a whispered speech.Specifically, the proposed model is more amendable to practical applicationsbecause it does not need to align speech features for training. Experimentalresults demonstrate that the proposed AGAN-W2SC can obtain improved speechquality and intelligibility compared with dynamic-time-warping-based methods.

Attention-Guided Generative Adversarial Network for Whisper to Normal Speech Conversion