Modern neural text-to-speech (TTS) synthesis can generate speech that is
indistinguishable from natural speech. However, the prosody of generated
utterances often represents the average prosodic style of the database instead
of having wide prosodic variation. Moreover, the generated prosody is solely
defined by the input text, which does not allow for different styles for the
same sentence. In this work, we train a sequence-to-sequence neural network
conditioned on acoustic speech features to learn a latent prosody space with
intuitive and meaningful dimensions. Experiments show that a model conditioned
on sentence-wise pitch, pitch range, phone duration, energy, and spectral tilt
can effectively control each prosodic dimension and generate a wide variety of
speaking styles, while maintaining similar mean opinion score (4.23) to our
Tacotron baseline (4.26).

通过使用一种基于序列对序列神经网络的方法，对音频语音特征进行条件训练，以学习一个直观且有意义的潜在韵律空间，进一步控制音调、音高、语音能量等特征参数，生成多种音色，并维持与 Tacotron 基线模型相似的评分（4.26/4.23）。

利用直觉的韵律特征进行可控的神经文本转语音合成

Controllable neural text-to-speech synthesis using intuitive prosodic  features

We introduce the Scratchpad Mechanism, a novel addition to the
sequence-to-sequence (seq2seq) neural network architecture and demonstrate its
effectiveness in improving the overall fluency of seq2seq models for natural
language generation tasks. By enabling the decoder at each time step to write
to all of the encoder output layers, Scratchpad can employ the encoder as a
"scratchpad" memory to keep track of what has been generated so far and thereby
guide future generation. We evaluate Scratchpad in the context of three
well-studied natural language generation tasks --- Machine Translation,
Question Generation, and Text Summarization --- and obtain state-of-the-art or
comparable performance on standard datasets for each task. Qualitative
assessments in the form of human judgements (question generation), attention
visualization (MT), and sample output (summarization) provide further evidence
of the ability of Scratchpad to generate fluent and expressive output.

通过引入刮板机制，作者对序列到序列 (seq2seq) 神经网络体系结构进行了改进，并且在自然语言生成任务中显示了其提高 seq2seq 模型整体流畅性的有效性。

保留笔记：使用蚂蚁笔记机制进行条件自然语言生成

Keeping Notes: Conditional Natural Language Generation with a Scratchpad  Mechanism

We present an attention-based sequence-to-sequence neural network which can
directly translate speech from one language into speech in another language,
without relying on an intermediate text representation. The network is trained
end-to-end, learning to map speech spectrograms into target spectrograms in
another language, corresponding to the translated content (in a different
canonical voice). We further demonstrate the ability to synthesize translated
speech using the voice of the source speaker. We conduct experiments on two
Spanish-to-English speech translation datasets, and find that the proposed
model slightly underperforms a baseline cascade of a direct speech-to-text
translation model and a text-to-speech synthesis model, demonstrating the
feasibility of the approach on this very challenging task.

该研究提出了一种基于注意力机制的端到端学习的序列到序列神经网络，能够直接将一种语言的语音翻译成另一种语言的语音，无需中间文本表示，该方法通过学习将语音谱图映射到目标语言的谱图，同时也演示了译后语音合成的能力。研究在两个西班牙语到英语的语音翻译数据集上进行了实验，证明了该方法在这个极具挑战的任务上的可行性。