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| Paper: | SP-L1.1 |
| Session: | Voice Conversion and Morphing Algorithms for TTS Systems |
| Time: | Tuesday, May 18, 15:30 - 15:50 |
| Presentation: |
Lecture |
| Topic: |
Speech Processing: Speech Synthesis (including TTS) |
| Title: |
NON-PARALLEL TRAINING FOR VOICE CONVERSION BY MAXIMUM LIKELIHOOD CONSTRAINED ADAPTATION |
| Authors: |
Athanasios Mouchtaris; University of Pennsylvania | | |
| | Jan Van der Spiegel; University of Pennsylvania | | |
| | Paul Mueller; Corticon Inc. | | |
| Abstract: |
The objective of voice conversion methods is to modify the speech characteristics of a particular speaker in such manner, as to sound like speech by a different target speaker. Current voice conversion algorithms are based on deriving a conversion function by estimating its parameters through a corpus that contains the same utterances spoken by both speakers. Such a corpus, usually referred to as a parallel corpus, has the disadvantage that many times it is difficult or even impossible to collect. Here, we propose a voice conversion method that does not require a parallel corpus for training, i.e. the spoken utterances by the two speakers need not be the same, by employing speaker adaptation techniques to adapt to a particular pair of source and target speakers, the derived conversion parameters from a different pair of speakers. We show that adaptation reduces the error obtained when simply applying the conversion parameters of one pair of speakers to another by a factor that can reach 30% in many cases, and with performance comparable with the ideal case when a parallel corpus is available. |
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