Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Differentiable Duration Modeling for End-to-End Text-to-Speech   Parallel text-to-speech (TTS) models have recently enabled fast and
highly-natural speech synthesis. However, such models typically require
external alignment models, which are not necessarily optimized for the decoder
as they are not jointly trained. In this paper, we propose a differentiable
duration method for learning monotonic alignments between input and output
sequences. Our method is based on a soft-duration mechanism that optimizes a
stochastic process in expectation. Using this differentiable duration method, a
direct text to waveform TTS model is introduced to produce raw audio as output
instead of performing neural vocoding. Our model learns to perform
high-fidelity speech synthesis through a combination of adversarial training
and matching the total ground-truth duration. Experimental results show that
our model obtains competitive results while enjoying a much simpler training
pipeline. Audio samples are available online.

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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph