15 research outputs found
Tacotron: Towards End-to-End Speech Synthesis
A text-to-speech synthesis system typically consists of multiple stages, such
as a text analysis frontend, an acoustic model and an audio synthesis module.
Building these components often requires extensive domain expertise and may
contain brittle design choices. In this paper, we present Tacotron, an
end-to-end generative text-to-speech model that synthesizes speech directly
from characters. Given pairs, the model can be trained completely
from scratch with random initialization. We present several key techniques to
make the sequence-to-sequence framework perform well for this challenging task.
Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English,
outperforming a production parametric system in terms of naturalness. In
addition, since Tacotron generates speech at the frame level, it's
substantially faster than sample-level autoregressive methods.Comment: Submitted to Interspeech 2017. v2 changed paper title to be
consistent with our conference submission (no content change other than typo
fixes
On Using Backpropagation for Speech Texture Generation and Voice Conversion
Inspired by recent work on neural network image generation which rely on
backpropagation towards the network inputs, we present a proof-of-concept
system for speech texture synthesis and voice conversion based on two
mechanisms: approximate inversion of the representation learned by a speech
recognition neural network, and on matching statistics of neuron activations
between different source and target utterances. Similar to image texture
synthesis and neural style transfer, the system works by optimizing a cost
function with respect to the input waveform samples. To this end we use a
differentiable mel-filterbank feature extraction pipeline and train a
convolutional CTC speech recognition network. Our system is able to extract
speaker characteristics from very limited amounts of target speaker data, as
little as a few seconds, and can be used to generate realistic speech babble or
reconstruct an utterance in a different voice.Comment: Accepted to ICASSP 201