Voice Conversion Using Exclusively Unaligned Training Data

Although all conventional voice conversion approaches require equivalent training utterances of source and target speaker, several recently proposed applications call for breaking this demand. In this paper, we present an algorithm which finds corresponding time frames within unaligned training data. The performance of this algorithm is tested by means of a voice conversion framework based on linear transformation of the spectral envelope. Experimental results are reported on a Spanish cross-gender corpus utilizing several objective error measures.