- The proposed system seems to deter the model in terms of their BLEU scores (system degrades in 2 out of the 3 settings). This leads me to think that while the model seems to do well on speaker specific terms/inflections, the overall translations degrade.
- How would we choose which ELM to pick (male/female)? Does this require us to know the speaker’s gender beforehand, i.e., at inference time? This seems like a drawback as the accuracy should be calculated after using a gender detection model in the pipeline (at least in the cases where vocal traits match speaker identity).
- What happens when a single audio file has two speakers (male and female) conversing with each other? Which ELM to pick in that case?
- How would we choose which ELM to pick (male/female)? Does this require us to know the speaker’s gender beforehand, i.e., at inference time? This seems like a drawback as the accuracy should be calculated after using a gender detection model in the pipeline (at least in the cases where vocal traits match speaker identity).