We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis.The system takes linguistic features as input, and employs neural networks to predict acoustic features, which are then passed to a vocoder to produce the speech waveform.Various neural network architectures are implemented, including a standard feedforward neural network, mixture density neural network, recurrent neural network (RNN), long short-term memory (LSTM) recurrent neural network, amongst others.The toolkit is Open Source, written in Python, and is extensible.This paper briefly describes the system, and provides some benchmarking results on a freelyavailable corpus.
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Wu et al. (2016) studied this question.
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