Two new embedding algorithms (similarity vector prediction and Frobenius-norm matrix matching) trained on subjective inter-speaker scores yield d-vectors more correlated with human similarity judgments and improve TTS quality for unseen speakers.
The algorithms used an inter-speaker similarity ma - trix obtained from large-scale subjective scoring as a cons traint on training the model
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DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis
Two new embedding algorithms (similarity vector prediction and Frobenius-norm matrix matching) trained on subjective inter-speaker scores yield d-vectors more correlated with human similarity judgments and improve TTS quality for unseen speakers.