DNN-based Speech Synthesis for Indian Languages from ASCII text
Table 3: Objective results of the proposed techniques versus the benchmark approach. MCD and BAP are Mel-Cepstral Distortion and Band Aperiodicity distortion, respectively. V/UV error means frame-level voiced/unvoiced prediction error. Root Mean Squared Error (RMSE) of F0 was calculated on a linear frequency scale.
['Uni-Grapheme-TTS', '4.97', '2.05', '35.13', '6.23', '4.82', '1.92', '10.87', '9.25']
We used following four objective evaluations to assess the performance of the proposed methods in comparison to the benchmark system. MCD: Mel-Cepstral Distortion (MCD) to measure MCC prediction performance. BAP: to measure distortion of BAPs F0 RMSE: Root Mean Squared Error (RMSE) to measure the accuracy of F0 prediction. The error value was calculated on a linear scale instead of log-scale which was used to model the F0 values. V/UV: to measure voiced/unvoiced error. In all these metrics, a lower value gives the better performance. While the objective metrics do not map directly to perceptual quality, they are often useful for system tuning. As expected, the benchmark model performs well on most metrics. While the G2P Model performs well on Telugu and Hindi, the Uni-Grapheme model does well on Tamil. Overall, the proposed approaches compare favourably with the benchmark.