Improving Speaker Consistency in Speech-to-Speech Translation Using Speaker Retention Unit-to-Mel Techniques

We propose a Speaker-Consistent Speech-to-Speech Translation (SC-S2ST) system that effectively retains speaker-specific information. While the paradigm of Speech-to-Unit Translation (S2UT) followed by Unit-to-Waveform Vocoder has become a mainstream for End-to-End S2ST systems, due to the substantial semantic content carried by discrete units, this approach primarily captures semantic information and often results in synthesized speech that lacks speaker-specific characteristics such as accent and individual voice qualities. Existing S2UT systems with style transfer face the issue of high inference latency. To address this limitation, we introduced a Speaker-Retention Unit-to-Mel (SR-UTM) framework designed to capture and preserve speaker-specific information. We conducted experiments on the CVSS-C and CVSS-T corpora for Spanish-English and French-English translation tasks. Our approach achieved BLEU scores of 16.10 and 21.68, which are comparable to those of the baseline S2UT system. Furthermore, our SC-S2UT system excelled in preserving speaker similarity. The speaker similarity experiments showed that our method effectively retains speaker-specific information without significantly increasing inference time. These results confirm that our primary approach successfully achieve speaker-consistent speech-to-speech translation.