We have implemented a deep learning model with L2 regularization and trained
it on Electromyography (EMG) data. The data comprises of EMG signals collected
from control group, myopathy and ALS patients. Our proposed deep neural network
consists of eight layers; five fully connected, two batch normalization and one
dropout layers. The data is divided into training and testing sections by
subsequently dividing the training data into sub-training and validation
sections. Having implemented this model, an accuracy of 99 percent is achieved
on the test data set. The model was able to distinguishes the normal cases
(control group) from the others at a precision of 100 percent and classify the
myopathy and ALS with high accuracy of 97.4 and 98.2 percents, respectively.
Thus we believe that, this highly improved classification accuracies will be
beneficial for their use in the clinical diagnosis of neuromuscular disorders.

本文基于电肌图数据实现了带有 L2 正则化的深度学习模型，在测试集上实现了 99% 的准确率，能够高精确度地对正常、肌无力和渐冻人病进行分类，有望为神经肌肉疾病的临床诊断提供帮助。

使用深度学习进行肌电信号分类

Electromyography Signal Classification Using Deep Learning

Automatic speech recognition (ASR) systems have dramatically improved over
the last few years. ASR systems are most often trained from 'typical' speech,
which means that underrepresented groups don't experience the same level of
improvement. In this paper, we present and evaluate finetuning techniques to
improve ASR for users with non-standard speech. We focus on two types of
non-standard speech: speech from people with amyotrophic lateral sclerosis
(ALS) and accented speech. We train personalized models that achieve 62% and
35% relative WER improvement on these two groups, bringing the absolute WER for
ALS speakers, on a test set of message bank phrases, down to 10% for mild
dysarthria and 20% for more serious dysarthria. We show that 71% of the
improvement comes from only 5 minutes of training data. Finetuning a particular
subset of layers (with many fewer parameters) often gives better results than
finetuning the entire model. This is the first step towards building state of
the art ASR models for dysarthric speech.

本文提出并评估了微调技术，以提高自动语音识别系统对非标准语音的识别准确性，特别关注两种非标准语音：肌萎缩侧索硬化症患者的言语和带口音的言语，并训练出个性化模型，相对于标准语音，相对词错误率能提高 62％和 35％，仅五分钟的训练就能带来 71％的提升，微调部分层的结果往往比微调整个模型更好，是构建肌无力性言语最先进的自动语音识别模型的第一步。

针对数据有限的发音不清和口音不标准的个性化 ASR

Personalizing ASR for Dysarthric and Accented Speech with Limited Data

We present a technique for significantly speeding up Alternating Least
Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor
factorization. By exploiting properties of the Khatri-Rao product, we show how
to efficiently address a computationally challenging sub-step of both
algorithms. Our algorithm, DFacTo, only requires two sparse matrix-vector
products and is easy to parallelize. DFacTo is not only scalable but also on
average 4 to 10 times faster than competing algorithms on a variety of
datasets. For instance, DFacTo only takes 480 seconds on 4 machines to perform
one iteration of the ALS algorithm and 1,143 seconds to perform one iteration
of the GD algorithm on a 6.5 million x 2.5 million x 1.5 million dimensional
tensor with 1.2 billion non-zero entries.

本文提出了一种名叫 DFacTo 的优化算法，利用 Khatri-Rao 乘积的特性，从而显著加速广泛应用于张量分解中的 ALS 和 GD 两个算法，且只需要进行两个稀疏矩阵向量乘积，易于并行化，且相较于其他算法平均快 4 至 10 倍，特别是在大规模数据集上。