Most neural vocoders employ band-limited mel-spectrograms to generate
waveforms. If full-band spectral features are used as the input, the vocoder
can be provided with as much acoustic information as possible. However, in some
models employing full-band mel-spectrograms, an over-smoothing problem occurs
as part of which non-sharp spectrograms are generated. To address this problem,
we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms
in real time. Inspired by works in the field of voice activity detection, we
added a multi-resolution spectrogram discriminator that employs multiple linear
spectrogram magnitudes computed using various parameter sets. Using full-band
mel-spectrograms as input, we expect to generate high-resolution signals by
adding a discriminator that employs spectrograms of multiple resolutions as the
input. In an evaluation on a dataset containing information on hundreds of
speakers, UnivNet obtained the best objective and subjective results among
competing models for both seen and unseen speakers. These results, including
the best subjective score for text-to-speech, demonstrate the potential for
fast adaptation to new speakers without a need for training from scratch.

该研究提出了一种名为 UnivNet 的神经声码器，利用全频带谱特征作为输入，通过添加多分辨率谱图鉴别器来解决非锐利音频信号生成的问题，并在大量演讲者信息数据集上取得最佳客观和主观结果。

UnivNet: 带多分辨率频谱鉴别器的神经声码器，用于高保真波形生成

UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram  Discriminators for High-Fidelity Waveform Generation

Synthesizing user-intended programs from a small number of input-output
examples is a challenging problem with several important applications like
spreadsheet manipulation, data wrangling and code refactoring. Existing
synthesis systems either completely rely on deductive logic techniques that are
extensively hand-engineered or on purely statistical models that need massive
amounts of data, and in general fail to provide real-time synthesis on
challenging benchmarks. In this work, we propose Neural Guided Deductive Search
(NGDS), a hybrid synthesis technique that combines the best of both symbolic
logic techniques and statistical models. Thus, it produces programs that
satisfy the provided specifications by construction and generalize well on
unseen examples, similar to data-driven systems. Our technique effectively
utilizes the deductive search framework to reduce the learning problem of the
neural component to a simple supervised learning setup. Further, this allows us
to both train on sparingly available real-world data and still leverage
powerful recurrent neural network encoders. We demonstrate the effectiveness of
our method by evaluating on real-world customer scenarios by synthesizing
accurate programs with up to 12x speed-up compared to state-of-the-art systems.

本文介绍了神经引导演绎搜索 (NGDS) 技术，应用于编写用户意图程序的问题，结合了符号逻辑技术和统计模型，通过演绎搜索框架来学习神经网络组件，以找到最适合提供的规范的程序，并在真实客户场景中验证其实用性。