We present a fully convolutional wav-to-wav network for converting between speakers' voices, without relying on text. Our network is based on an encoder-decoder architecture, where the encoder is pre-trained for the task of Automatic Speech Recognition (ASR), and a multi-speaker waveform decoder is trained to reconstruct the original signal in an autoregressive manner. We train the network on narrated audiobooks, and demonstrate the ability to perform multi-voice TTS in those voices, by converting the voice of a TTS robot. We observe no degradation in the quality of the generated voices, in comparison to the reference TTS voice. The modularity of our approach, which separates the target voice generation from the TTS module, enables client-side personalized TTS in a privacy-aware manner.
In this work, we rely on a trained ASR network in order to obtain features. The ASR features are supposedly mostly orthogonal to the identity. An alternative approach would be to use the speaker identification features, in order to represent the source and target voices and perform the transformation between the two. I-vectors are a GMM-based representation, which is often used in speaker identification or verification. @cite_10 have aligned the source and target GMMs, by comparing the i-vectors of the two speakers, without using transcription or parallel data. Unlike our method, the reference speaker is known at training time, and their method employs an MFCC vocoder, which limits the output's quality, in comparison to WaveNets. Speaker verification features were also used to embed speakers by @cite_7 . In this case, the speaker embedding is based on a neural classifier, and is used within a tacotron2 @cite_19 TTS framework, which employs a WaveNet decoder.
@cite_19 @cite_10 @cite_7
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This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of 4.53 comparable to a MOS of 4.58 for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the conditioning input to WaveNet instead of linguistic, duration, and @math features. We further show that using this compact acoustic intermediate representation allows for a significant reduction in the size of the WaveNet architecture. Text-independent speaker verification (recognizing speakers regardless of content) and non-parallel voice conversion (transforming voice identities without requiring content-matched training utterances) are related problems. We adopt i-vector method to voice conversion. An i-vector is a fixed-dimensional representation of a speech utterance that enables treating voice conversion in utterance domain, as opposed to frame domain. The high dimensionality (800) and small number of training utterances (24) necessitates using prior information of speakers. We adopt probabilistic linear discriminant analysis (PLDA) for voice conversion. The proposed approach requires neither parallel utterances, transcriptions nor time alignment procedures at any stage. Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of posterior collapse'' -— where the latents are ignored when they are paired with a powerful autoregressive decoder -— typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.