PHONEME DEPENDENT SPEAKER EMBEDDING AND MODEL FACTORIZATION FOR MULTI-SPEAKER SPEECH SYNTHESIS AND ADAPTATION

This paper presents an architecture to perform speaker adaption in long short-term memory (LSTM) based Mandarin statistical parametric speech synthesis system. Compared with the conventional methods that focused on using fixed global speaker representations in utterance level for speaker recognition task, the proposed method extracts speaker representations in utterance and phoneme level, which can describe more pronunciation characteristics in phoneme level. And an attention mechanism is deployed to combine each level representations dynamically to train a task-specific phoneme dependent speaker embedding. To handle the unbalanced database and avoid over-fitting, the model is factored into an average model and an adaptation model and combined by an attention mechanism. We investigate the performance of speaker representations extracted by different methods. Experimental results confirm the adaptability of our proposed speaker embedding and model factorization structure. And listening tests demonstrate that our proposed method can achieve better adaptation performance than baselines in terms of naturalness and speaker similarity.