60,464 research outputs found
Forward Attention in Sequence-to-sequence Acoustic Modelling for Speech Synthesis
This paper proposes a forward attention method for the sequenceto- sequence
acoustic modeling of speech synthesis. This method is motivated by the nature
of the monotonic alignment from phone sequences to acoustic sequences. Only the
alignment paths that satisfy the monotonic condition are taken into
consideration at each decoder timestep. The modified attention probabilities at
each timestep are computed recursively using a forward algorithm. A transition
agent for forward attention is further proposed, which helps the attention
mechanism to make decisions whether to move forward or stay at each decoder
timestep. Experimental results show that the proposed forward attention method
achieves faster convergence speed and higher stability than the baseline
attention method. Besides, the method of forward attention with transition
agent can also help improve the naturalness of synthetic speech and control the
speed of synthetic speech effectively.Comment: 5 pages, 3 figures, 2 tables. Published in IEEE International
Conference on Acoustics, Speech and Signal Processing 2018 (ICASSP2018
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
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
We summarize the accomplishments of a multi-disciplinary workshop exploring
the computational and scientific issues surrounding the discovery of linguistic
units (subwords and words) in a language without orthography. We study the
replacement of orthographic transcriptions by images and/or translated text in
a well-resourced language to help unsupervised discovery from raw speech.Comment: Accepted to ICASSP 201
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