  Whispered speech is a special way of pronunciation without using vocal cord
vibration. A whispered speech does not contain a fundamental frequency, and its
energy is about 20dB lower than that of a normal speech. Converting a whispered
speech into a normal speech can improve speech quality and intelligibility. In
this paper, a novel attention-guided generative adversarial network model
incorporating an autoencoder, a Siamese neural network, and an identity mapping
loss function for whisper to normal speech conversion (AGAN-W2SC) is proposed.
The proposed method avoids the challenge of estimating the fundamental
frequency of the normal voiced speech converted from a whispered speech.
Specifically, the proposed model is more amendable to practical applications
because it does not need to align speech features for training. Experimental
results demonstrate that the proposed AGAN-W2SC can obtain improved speech
quality and intelligibility compared with dynamic-time-warping-based methods.
