  Neural vocoders are now being used in a wide range of speech processing
applications. In many of those applications, the vocoder can be the most
complex component, so finding lower complexity algorithms can lead to
significant practical benefits. In this work, we propose FARGAN, an
autoregressive vocoder that takes advantage of long-term pitch prediction to
synthesize high-quality speech in small subframes, without the need for
teacher-forcing. Experimental results show that the proposed 600~MFLOPS FARGAN
vocoder can achieve both higher quality and lower complexity than existing
low-complexity vocoders. The quality even matches that of existing
higher-complexity vocoders.
