Dysarthria speech contains the pathological characteristics of vocal tract
and vocal fold, but so far, they have not yet been included in traditional
acoustic feature sets. Moreover, the nonlinearity and non-stationarity of
speech have been ignored. In this paper, we propose a feature enhancement
algorithm for dysarthria speech called WHFEMD. It combines empirical mode
decomposition (EMD) and fast Walsh-Hadamard transform (FWHT) to enhance
features. With the proposed algorithm, the fast Fourier transform of the
dysarthria speech is first performed and then followed by EMD to get intrinsic
mode functions (IMFs). After that, FWHT is used to output new coefficients and
to extract statistical features based on IMFs, power spectral density, and
enhanced gammatone frequency cepstral coefficients. To evaluate the proposed
approach, we conducted experiments on two public pathological speech databases
including UA Speech and TORGO. The results show that our algorithm performed
better than traditional features in classification. We achieved improvements of
13.8% (UA Speech) and 3.84% (TORGO), respectively. Furthermore, the
incorporation of an imbalanced classification algorithm to address data
imbalance has resulted in a 12.18% increase in recognition accuracy. This
algorithm effectively addresses the challenges of the imbalanced dataset and
non-linearity in dysarthric speech and simultaneously provides a robust
representation of the local pathological features of the vocal folds and
tracts.

本文提出了一个用于改善发音障碍语音的特征增强算法，名为 WHFEMD。该算法结合了经验模态分解（EMD）和快速 Walsh-Hadamard 变换（FWHT），用于提取和增强特征，以及表示声带和声道的局部病理特征。在实验中，我们将该算法应用于两个公共病理语音数据库，并取得了显著的分类性能提升和识别准确率的增加。

利用经验模态分解和 Walsh-Hadamard 变换增强发音障碍语音特征表征

Enhancing dysarthria speech feature representation with empirical mode  decomposition and Walsh-Hadamard transform

We study the problem of estimating $k$-ary distributions under
$\varepsilon$-local differential privacy. $n$ samples are distributed across
users who send privatized versions of their sample to a central server. All
previously known sample optimal algorithms require linear (in $k$)
communication from each user in the high privacy regime $(\varepsilon=O(1))$,
and run in time that grows as $n\cdot k$, which can be prohibitive for large
domain size $k$.
We propose Hadamard Response (HR}, a local privatization scheme that requires
no shared randomness and is symmetric with respect to the users. Our scheme has
order optimal sample complexity for all $\varepsilon$, a communication of at
most $\log k+2$ bits per user, and nearly linear running time of $\tilde{O}(n +
k)$.
Our encoding and decoding are based on Hadamard matrices, and are simple to
implement. The statistical performance relies on the coding theoretic aspects
of Hadamard matrices, ie, the large Hamming distance between the rows. An
efficient implementation of the algorithm using the Fast Walsh-Hadamard
transform gives the computational gains.
We compare our approach with Randomized Response (RR), RAPPOR, and
subset-selection mechanisms (SS), both theoretically, and experimentally. For
$k=10000$, our algorithm runs about 100x faster than SS, and RAPPOR.

该论文提出了一种名为 Hadamard Response 的本地隐私保护方案，可在高隐私水平下使用，并在所有 ε 下具有最优的样本复杂度，通信不超过每个用户的 log k + 2 位，并且具有几乎线性的运行时间，该方案的编码和解码基于 Hadamard 矩阵，其统计性能依赖于编码理论方面的因素。