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The style of the speech varies from person to person and every person exhibits his or her own style of speaking that is determined by the language, geography, culture and other factors. Style is best captured by prosody of a signal. High quality multi-speaker speech synthesis while considering prosody and in a few shot manner is an area of active r...
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... Transformer The architecture of FeedForward Transformer (Fig. 2) is based on a multi-head selfattention network, and position feed-forward network which consists of two Conv1D and normalization stages. The proposed method stacks multiple FFT blocks with phoneme embedding and position encoding as an input as the phoneme side, and multiple FFT blocks for the melspectrogram generation, with variance ...
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... through two proposed approaches: based on convolution network and multi-head attention network. This helps in adjusting the bias and scale of the normalized features to learn the required properties of speech signal including prosody. This module enables adaptive instance normalization of the feature map coming as output from the prior FFT block (Fig. ...
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... and energy of the reference speech sample to generate a tensor of size (audio-frames * 258 * batches). This is then fed it into the multi-head attention network to generate the affine parameters. These affine parameters (Equation (3)) are used to bias β attention and scale γ attention the output feature map coming from previous FFT block (Fig. ...
