We propose prosody embeddings for emotional and expressive speech synthesis networks. The proposed methods introduce temporal structures in the embedding networks, which enable fine-grained control of the speaking style of the synthesized speech. The temporal structures could be designed either in speech-side or text-side, which lead different control resolution in time. The prosody embedding networks are plugged into end-to-end speech synthesis networks, and trained without any other supervision except the target speech for synthesizing. The prosody embedding networks learned to extract prosodic features. By adjusting the learned prosody features, we could change the pitch and amplitude of the synthesized speech both in frame level and phoneme level. We also introduce temporal normalization of prosody embeddings, which shows better robustness against speaker perturbation in prosody transfer tasks.

Abstract: This paper describes a fast speaker search system to retrieve segments of the same voice identity in the large-scale data. Locality Sensitive Hashing (LSH) is a fast nearest neighbor search algorithm and the recent study shows that LSH enables quick retrieval of a relevant voice in the large-scale data in conjunction with i-vector while maintaining accuracy. In this paper, we proposed Random Speaker-variability Subspace (RSS) projection to map a data into hash tables. We hypothesized that rather than projecting on random subspace, projecting on speaker variability space would give more chance to put the same speaker representation into the same hash bins, so we can use less number of hash tables. We use Linear Discriminant Analysis (LDA) to generate speaker variability subspace projection matrix. Additionally, a random subset of the speaker in the training data was chosen for speaker label for LDA to produce multiple RSS. From the experimental result, the proposed approach shows 100 times and 7 times faster than the linear search and LSH, respectively.

Abstract: Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronunciation often reflect their native language's characteristics. This motivates us to investigate how the speech synthesis network learns the pronunciation from datasets from different languages. In this study, we are interested in analyzing and taking advantage of multilingual speech synthesis network. First, we train the speech synthesis network bilingually in English and Korean, and analyze how the network learns the relations of phoneme pronunciation between the languages. Our experimental result shows that the learned phoneme embedding vectors are located closer if their pronunciations are similar across the languages. Consequently, the trained networks can synthesize English speaker's Korean speech and vice versa. Using this result, we propose a training framework to utilize information of different language. To be specific, we pre-train a speech synthesis network using dataset from both high-resource language and low-resource language, then we fine-tune the network using the low-resource language dataset. Finally, we conducted more simulations on 10 different languages to show it is generally extendable to other languages.

Abstract: We propose a neural text-to-speech (TTS) model that can imitate a new speaker's voice using only a small amount of speech sample. We demonstrate voice imitation using only a 6-seconds long speech sample without any other information such as transcripts. Our model also enables voice imitation instantly without additional training of the model. We implemented the voice imitating TTS model by combining a speaker embedder network with a state-of-the-art TTS model, Tacotron. The speaker embedder network takes a new speaker's speech sample and returns a speaker embedding. The speaker embedding with a target sentence are fed to Tacotron, and speech is generated with the new speaker's voice. We show that the speaker embeddings extracted by the speaker embedder network can represent the latent structure in different voices. The generated speech samples from our model have comparable voice quality to the ones from existing multi-speaker TTS models.