**PASE+** is a problem-agnostic speech encoder that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-supervised problems (i.e., ones that do not require manual annotations as ground truth). An online speech distortion module is employed, that contaminates the input signals with a variety of random disturbances. A revised encoder is also proposed that better learns short- and long-term speech dynamics with an efficient combination of recurrent and convolutional networks. Finally, the authors refine the set of workers used in self-supervision to encourage better cooperation.

**Layer-wise Adaptive Rate Scaling**, or **LARS**, is a large batch optimization technique.  There are two notable differences between LARS and other adaptive algorithms such as [Adam](https://paperswithcode.com/method/adam) or [RMSProp](https://paperswithcode.com/method/rmsprop): first, LARS uses a separate learning rate for each layer and not for each weight. And second, the magnitude of the update is controlled with respect to the weight norm for better control of training speed.

$$m\_{t} = \beta\_{1}m\_{t-1} + \left(1-\beta\_{1}\right)\left(g\_{t} + \lambda{x\_{t}}\right)$$
$$x\_{t+1}^{\left(i\right)} = x\_{t}^{\left(i\right)}  - \eta\_{t}\frac{\phi\left(|| x\_{t}^{\left(i\right)} ||\right)}{|| m\_{t}^{\left(i\right)} || }m\_{t}^{\left(i\right)} $$

LARS

Large Batch Training of Convolutional Networks

PASE+

Multi-task self-supervised learning for Robust Speech Recognition

**fastText** embeddings exploit subword information to construct word embeddings. Representations are learnt of character $n$-grams, and words represented as the sum of the $n$-gram vectors. This extends the word2vec type models with subword information. This helps the embeddings understand suffixes and prefixes. Once a word is represented using character $n$-grams, a skipgram model is trained to learn the embeddings.

Source	Multi-task self-supervised learning for Robust Speech Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com