Any-to-any voice conversion using representation separation auto-encoder
In view of the problem that it was difficult to separate speaker personality characteristics from semantic con-tent information in any-to-any voice conversion under non-parallel corpus,which led to unsatisfied performance,a voice conversion method,called RSAE-VC(representation separation auto-encoder voice conversion)was proposed.The speaker's personality characteristics in the speech were regarded as time invariant and the content information as time variant,and the instance normalization and activation guidance layer were used in the encoder to separate them from each other.Then the content information of the source speech and the personality characteristics of the target one was utilized to synthesize the converted speech by the decoder.The experimental results demonstrate that RSAE-VC has an average reduction of 3.11%and 2.41%in Mel cepstral distance and root mean square error of pitch frequency respectively,and has an increasement of 5.22%in MOS and 8.45%in ABX,compared with the AGAIN-VC(activation guidance and adaptive instance normalization voice conversion)method.In RSAE-VC,self-content loss is applied to make the con-verted speech reserve more content information,and self-speaker loss is used to separate the speaker personality charac-teristics from the speech better,which ensure the speaker personality characteristics be left in the content information as little as possible,and the conversion performance is improved.
voice conversionrepresentation separationadaptive instance normalizationself-content lossself-speaker loss
万方数据
维普
