  The perceptual quality of neural text-to-speech (TTS) is highly dependent on
the choice of the model during training. Selecting the model using a
training-objective metric such as the least mean squared error does not always
correlate with human perception. In this paper, we propose an objective metric
based on the phone error rate (PER) to select the TTS model with the best
speech intelligibility. The PER is computed between the input text to the TTS
model, and the text decoded from the synthesized speech using an automatic
speech recognition (ASR) model, which is trained on the same data as the TTS
model. With the help of subjective studies, we show that the TTS model chosen
with the least PER on validation split has significantly higher speech
intelligibility compared to the model with the least training-objective metric
loss. Finally, using the proposed PER and subjective evaluation, we show that
the choice of best TTS model depends on the genre of the target domain text.
All our experiments are conducted on a Hindi language dataset. However, the
proposed model selection method is language independent.
