In this post, you would learn about a neural network reference solution architecture which could be used to convert the text to speech. The neural network solution architecture given in this post is based on deep learning (autoencoder network (encoder-decoder) with attention).
In the solution architecture diagram (figure 1) depicted below, the following is described:
One of the emerging areas of AI / machine learning is the ability to clearly convert text to speech. In this field, deep learning has been extensively used to come up with unique and effective solutions. One of the solution architecture, as discussed in this post, makes use of converting the text into character embeddings and passing the embeddings through sequence-to-sequence prediction network (encoder-attention-decoder deep neural networks). The network converts the character embeddings into the spectrogram which could be further passed through Wavenet like the deep neural network to convert into raw human-like audio (time-domain waveforms) signals.
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