Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion
Table 4: Performance of different encoding dimensions.
['[BOLD] Method', '[BOLD] Dim', '[BOLD] CER', '[BOLD] BR', '[BOLD] ABX', '[BOLD] distinct']
The CER column is the output Character Error Rate (CER) from a pre-trained ASR, where we use the ASR results of real input voice as ground truth, which measures intelligibility of the converted speech. The distinct column indicates the number of unique symbols used to encode speech in the test set. Lower values suggest a better performance for all the measures described above. When compared to other approaches, the proposed method achieves lower BR and distinct values with comparable ABX scores. Due to the discrete and differentiable nature of MBV, the proposed method can be used in other unsupervised end-to-end clustering or classification tasks, where other approaches may fail to generalize.