Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
Table 1: Quantitative Comparison of Style Conversion Quality for In-Dataset and Out-of-Dataset Target Speakers
['Target Speakers', 'Accuracy (Percent) Top-1', 'Accuracy (Percent) Top-3', 'Accuracy (Percent) Top-5', 'Accuracy (Percent) Top-10', 'Accuracy (Percent) Top-20']
Given these challenges, we opted to use a more scalable method to compare the out-of-dataset target conversion quality to in-dataset target conversions. We trained an i-Vector based speaker identification (SID) model as described in [ MFCC parameters are set as follows: fs=16KHz, framelength=25ms, frameshift=10ms, lowfreq=40, highfreq=7800, numceps=20, vtlnlow=60, vtlnhigh=7200. SID model is trained on all 291 speakers from train-clean-100 and dev-clean datasets (Sec. Conversion model has no knowledge of the SID model and is not specifically trained to perform adversarial attacks on it. Top-K accuracy measures if the conversion target ’s score is among the highest ranked K scores. When presented to the SID system, 9.7% of the converted utterances are predicted to come from the intended target (among 291 potential target speakers). Average accuracy for out-of-dataset targets is lower at 4.8%, but significantly higher than random chance (0.3%); demonstrating that FE can generate reasonable embeddings (style) for new speakers.