**WaveTTS** is a [Tacotron](https://paperswithcode.com/method/tacotron)-based text-to-speech architecture that has two loss functions: 1) time-domain loss, denoted as the waveform loss, that measures the distortion between the natural and generated waveform; and 2) frequency-domain loss, that measures the Mel-scale acoustic feature loss between the natural and generated acoustic features.

The motivation arises from [Tacotron 2](https://paperswithcode.com/method/tacotron-2). Here its feature prediction network is trained independently of the [WaveNet](https://paperswithcode.com/method/wavenet) vocoder. At run-time, the feature prediction network and WaveNet vocoder are artificially joined together. As a result, the framework suffers from the mismatch between frequency-domain acoustic features and time-domain waveform. To overcome such mismatch, WaveTTS uses a joint time-frequency domain loss for TTS that effectively improves the synthesized voice quality.

**DeCLUTR** is an approach for learning universal sentence embeddings that utilizes a self-supervised objective that does not require labelled training data. The objective learns universal sentence embeddings by training an encoder to minimize the distance between the embeddings of textual segments randomly sampled from nearby in the same document.

DeCLUTR

DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

WaveTTS

WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss

**TSRUp**, or **Transformation-based Spatial Recurrent Unit p**, is a modification of a [ConvGRU](https://paperswithcode.com/method/cgru) used in the [TriVD-GAN](https://paperswithcode.com/method/trivd-gan) architecture for video generation.

It largely follows [TSRUc](https://paperswithcode.com/method/tsruc), but computes $\theta$, $u$ and $c$ in parallel given $x\_{t}$ and $h\_{t−1}$, yielding the following replacement for the $c$ update equation:

$$ c = \rho\left(W\_{c} \star\_{n}\left[h\_{t-1}; x\_{t}\right] + b\_{c} \right) $$

In these equations $\sigma$ and $\rho$ are the elementwise sigmoid and [ReLU](https://paperswithcode.com/method/relu) functions respectively and the $\star\_{n}$ represents a [convolution](https://paperswithcode.com/method/convolution) with a kernel of size $n \times n$. Brackets are used to represent a feature concatenation.

Source	WaveTTS: Tacotron-based TTS with Joint Time-Frequency Domain Loss
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com