  Recently, autoregressive neural vocoders have provided remarkable performance
in generating high-fidelity speech and have been able to produce synthetic
speech in real-time. However, autoregressive neural vocoders such as WaveFlow
are capable of modeling waveform signals from mel-spectrogram, its number of
parameters is significant to deploy on edge devices. Though NanoFlow, which has
a small number of parameters, is a state-of-the-art autoregressive neural
vocoder, the performance of NanoFlow is marginally lower than WaveFlow.
Therefore, we propose a new type of autoregressive neural vocoder called
FlowVocoder, which has a small memory footprint and is capable of generating
high-fidelity audio in real-time. Our proposed model improves the density
estimation of flow blocks by utilizing a mixture of Cumulative Distribution
Functions (CDF) for bipartite transformation. Hence, the proposed model is
capable of modeling waveform signals, while its memory footprint is much
smaller than WaveFlow. As shown in experiments, FlowVocoder achieves
competitive results with baseline methods in terms of both subjective and
objective evaluation, also, it is more suitable for real-time text-to-speech
applications.
