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Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network
EURASIP Journal on Audio, Speech, and Music Processing volume 2020, Article number: 1 (2020)
Abstract
In this paper, we use empirical mode decomposition and Hurstbased mode selection (EMDH) along with deep learning architecture using a convolutional neural network (CNN) to improve the recognition of dysarthric speech. The EMDH speech enhancement technique is used as a preprocessing step to improve the quality of dysarthric speech. Then, the Melfrequency cepstral coefficients are extracted from the speech processed by EMDH to be used as input features to a CNNbased recognizer. The effectiveness of the proposed EMDHCNN approach is demonstrated by the results obtained on the Nemours corpus of dysarthric speech. Compared to baseline systems that use Hidden Markov with Gaussian Mixture Models (HMMGMMs) and a CNN without an enhancement module, the EMDHCNN system increases the overall accuracy by 20.72% and 9.95%, respectively, using a kfold crossvalidation experimental setup.
Introduction
Dysarthria is a motor speech disorder resulting from diverse impairments afflicting the control and execution of speech movements. A lack of coordination or a weakness of the muscles required for speech is then noticed. The main consequence of dysarthria is the degradation of speech intelligibility caused by poor articulation of consonants and, in the most severe cases, by the distortion of vowels [1]. Due to these important variations, a speech recognition system specifically tailored to dysarthric speakers would be more suitable than a generic system [2–4].
Related work
Most conventional dysarthric speech recognition systems are based on structured approaches. For instance, HMMGMMbased approaches [5] use Hidden Markov Models (HMMs) to model the sequential structure of the speech signal and Gaussian Mixture Models (GMMs) to model the distribution of the spectral representation of a waveform. However, HMMGMMbased systems require a large amount of data to be trained, which is not efficient in the case of dysarthric speech where the corpora used for training are always small [6]. Therefore, these approaches cannot be applied with ease in the context of dysarthric speech.
In recent years, the field of pathological speech processing has seen major breakthroughs in effective alternatives to HMMGMM that can better recognize dysarthric speech thanks to the development of deep neural network (DNN) architectures. For instance, the work by Kim et al. [4] adopts convolutional long shortterm memory recurrent neural networks to model dysarthric speech in a speakerindependent way, taking advantage of convolutional neural networks to extract effective local features. The approach in Vachhani et al. [7] uses healthy speech augmented with simulated dysarthric speech to train a DNNHMMbased automatic speech recognition. Bhat et al. [8] proposed using a timedelay neural networkbased denoising autoencoder to enhance dysarthric speech features before performing DNNHMM–based recognition.
Liu et al. [9] investigated the use of pitch features for disordered speech recognition. They attempt to use gated neural network and Bayesian gated neural network models to explore the robust integration of pitch features to improve the recognition performance for disordered speech. The authors in [10] used a multilayer perceptron (MLP) artificial neural network for diagnostic testing of speech affected by Parkinson’s disease. He used a relatively large dataset of speech sample features compiled from Parkinson’s disease patients and healthy individuals to train the MLP system to accurately classify between these two speech types. The dysarthric speech recognition system proposed by Hu et al. [11] is based on a gated neural network (GNN) and allows robust integration of acoustic features with visual features as well as, optionally, prosody features based on pitch. They designed two systems for English and Cantonese languages where a speakerindependent GNN acoustic model was trained for the mixed training set using a concatenated 86dimension log filter bank plus pitch features.
The authors in [12] investigated the potential correlation between noisy speech and dysarthric speech in terms of intelligibility. They specifically examined whether there is a relationship between the ability of an individual to deal with noisy speech uttered in severely degraded environments and his/her ability to understand dysarthric speech. The metrics used revealed that listeners who were able to understand speech in noise also succeeded in understanding dysarthric speech. Although the origin of the degradation differs, we can reasonably consider that the speech in noise results from the degradation of the environment, while dysarthric speech results from the degradation of the source.
Motivation and objective of the study
In this paper, dysarthric speech is viewed as noisy speech, and therefore, we recommend the use of speech enhancement techniques to improve its quality and thus its intelligibility. A combination of a robust variant of empirical mode decomposition (EMD) with a convolutional neural network is proposed to perform an accurate phonemic recognition of dysarthric speech.
The motivation underlying the use of EMD is its ability to produce a timefrequency representation of nonstationary signals using an adaptive decomposition of the signals in the sum of oscillating components called intrinsic mode functions (IMFs). This decomposition has the appearance of a generalized Fourier analysis with variable amplitudes and frequencies. The frequency resolution of EMD for any point is uniformly defined by the stationary phase or local derivative of the phase, which makes this method appropriate for the effective extraction of lowfrequency oscillations that are often observed in dysarthric speech.
The rest of this paper is organized as follows. Section 2 presents the Hurst exponent, the empirical mode decomposition, and the Hurstbased mode selection (EMDH) used to enhance the quality of dysarthric speech. The architecture of the proposed system and the HMMGMM and CNNbased recognizers are depicted in this section. Section 3 describes the experimental setup, the data in the Nemours corpus used in the experiments, and the obtained results. Section 4 concludes the paper and provides an overview of future work.
Proposed method
Hurst exponent estimation
The Hurst exponent H was named after the British hydrologist Harold Edwin Hurst, who studied the longterm storage capacity of reservoirs [13] and discovered the presence of longterm dependence in hydrology. Many years later, the mathematician Benoit Mandelbrot [14] generalized the Hurst exponent as a measure of the longterm memory of a time series, i.e., the tendency of a time series to strongly regress towards its mean or to cluster in one direction.
Since then, the Hurst exponent has been widely used as a measure of time dependence in various time series. The estimate of its value gives a hint on the persistence/autocorrelation in the related time series: if 0<H<0.5, the series is antipersistent or has a negative autocorrelation; if H=0.5, there is no autocorrelation in the series; and if 0.5<H<1, the series is persistent and has a positive autocorrelation.
In the spectral domain, the Hurst exponent is related to the spectral characteristics of the time series [15]. For H=0.5, the time series has equal intensity at different frequencies, and the power spectral density S(f) of the signal is constant, e.g., white noise; on the other hand, if 0.5<H<1, low frequencies are prominent, especially when H is closer to 1, i.e., S(f) is inversely proportional to the frequency of the signal (pink noise).
Due to the characteristics above, the Hurst exponent estimation has been applied in different areas ranging from hydrology to speech processing. For instance, the authors in [16] composed speech vectors based on the Hurst exponent to perform speaker recognition, and the authors in [15] used the Hurst exponent to estimate noise and enhance speech signals.
Different Hurst exponent estimation methods exist, but this work uses discrete wavelet transforms (DWT) as in [15]. Each IMF is split into nonoverlapping frames, and the steps below briefly describe how the discrete wavelet transform is used to estimate the Hurst exponent:
 1
Apply the discrete wavelet transform:
A time domain signal is decomposed successively by the DWT into approximation a_{j}(n) and detail d_{j}(n) coefficients, where j is the decomposition scale and n is the coefficient index of each scale.
 2
Estimate the variance σj^{2}:
Denoising techniques based on DWT exploit the detail coefficients to produce a smoother signal. Hence, the variance is estimated from those coefficients as:
$$\sigma{j}^{2} = (1/N_{j})\sum{_{n}} d_{j}(n)^{2}$$where N_{j} is the number of available coefficients for each scale j.
 3
Calculate the Hurst exponent:
The Hurst exponent is estimated as H=(1+θ)/2, where θ is the slope of the plot of
$$y_{j} = log{_{2}}(\sigma{j}^{2})$$versus j. The slope θ is obtained with a weighted linear regression.
EMDH for enhancing dysarthric speech
The principle of the EMDH speech enhancement technique consists of identifying and selecting the intrinsic mode functions (IMFs) that are less distorted and using them to reconstruct the enhanced speech signal [15].
First, EMDH starts with the decomposition of a dysarthric speech signal into a set of IMFs. Then, each IMF is windowed into nonoverlapping shorttime frames to determine the most distorted IMF by calculating the Hurst exponent for each IMF and using the waveletbased method to generate Daubechies filters [15]. This framebyframe analysis is performed to avoid the incorrect selection of IMF_{i} due to the extreme variability and nonstationarity of the signal. Finally, the frames with Hurst exponent values greater than a given threshold H_{th} are dropped, and the remaining frames are used to reconstruct the enhanced dysarthric speech. The EMDH steps are as follows:
 1
Use the EMD algorithm to decompose a dysarthric speech signal x(t) into a finite number of IMF_{s}. If M IMF_{s} are extracted and the m^{th} is denoted as IMF_{m}, then
$$ \left[x(t) = \sum_{m=1}^{M} IMF_{m}(t) + r(t)\right] $$(1)where r(t) is the residual signal remaining after the decomposition.
 2
Split each IMF_{m} into Q nonoverlapping shorttime frames. We obtain the following window IMF_{m} (wIMF_{m}):
$$ wIMF_{m,q}(t) = \left\{\begin{array}{ll} IMF_{m}(t+qT_{d}), & \ t\in[\!0,T_{d}]\\ 0, &\ \text{elsewhere}\end{array} \right\} $$(2)where q∈{0,…,Q−1} is the frame index and T_{d} is the fixed time duration of the frames.
 3
Apply wavelet decomposition to all wIMF_{m}.
Estimate their Hurst exponents and store them in a vector of Hurst values H_{q}(m) with M components (m∈{1,…,M}) calculated for each index q.
 4
Determine for each frame the index N_{q} of the last wIMF_{m} whose Hurst value is below a given threshold: H_{q}(N_{q})<H_{th}.
 5
Reconstruct the enhanced speech signal \(\hat {x}(t)\) as
$$ \hat{x}(t) = {\sum\nolimits}_{q=0}^{Q1} \hat{x}_{q}(tqT_{d}) $$(3)and
$$ \hat{x}_{q}(t) = {\sum\nolimits}_{m=1}^{N_{q}} wIMF_{m,q}(t) $$(4)q∈{0,…,Q−1}.
We set M to 10 and H_{th} to 0.90 in the experiments.
To show the effect of the EMDH technique, we conducted enhancement experiments on dysarthric speech using spectral subtraction, Wiener filtering, and EMDH techniques. The analysis of the obtained results showed that EMDH always gives the higher segmental signaltonoise ratio (SNRseg). The perceptual evaluation of speech quality (PESQ) metric also illustrated the superiority of EMDH over the two other techniques. Therefore, we adopted EMDH as a preprocessing step in our proposed system. Table 1 gives the results of the enhancement experiment using a sentence from dysarthric speaker JF.
HMMGMM system
HMMs model the sequential structure of the speech signal, and GMMs model the distribution of the spectral representation of the signal using different Gaussian distributions.
The HMMGMM speech recognition system is built using HTK tools [5], where each phoneme is modeled by a 5state HMM model with 2 nonemitting states (the first and fifth states) and a mixture of 2, 4, 8, or 16 Gaussian distributions. Melfrequency cepstral coefficients (MFCCs), delta coefficients, and the cepstral pseudoenergy are calculated for all utterances and used as parameters to train and test the system. Figure 1 depicts the training and test phases of this system.
We used this HMMGMM system as a baseline for comparison with the EMDHCNN system.
CNN system
Convolutional neural networks (CNNs) perform the mathematical operation called convolution instead of general matrix multiplication to produce filtered feature cards stacked on the top of each other. A CNN has the following components [17]:
 1
Convolutional layer:
This layer extracts characteristics from the input features, and its output feature maps are given by \( C(x_{u,v}) = \sum _{i=\frac {n}{2}}^{\frac {n}{2}} \sum _{i=\frac {m}{2}}^{\frac {m}{2}} f_{k}(i, j) \ x_{ui, vj} \) where f_{k} is the filter with kernel size n×m applied to the input x, and n×m is the number of input connections to each CNN neuron.
 2
Pooling layer:
The pooling layer is a downsampling strategy applied to the output feature maps of the convolutional layer to reduce their number and make them more invariant to changes in scale and orientation. Two common pooling functions are used to reduce the number of subregions: average pooling and max pooling. We use the max pooling function. \( M(x_{i}) = \text {max} \{x_{i+k, i+l} \ k \leq \frac {m}{2}, \ l \leq \frac {m}{2}, \ k,l \in N\} \) where x is the input and m is the size of the max filter.
 3
ReLU (rectified linear unit):
This activation function replaces all negative values in the feature map with zero. The main goal of ReLU is to introduce nonlinearity into the CNN system because most of the data we would like the system to learn would be nonlinear. ReLU is defined by the equation R(x)=max(0,x), where x is the input.
Other functions are used, such as tanh or sigmoid, but ReLU has been shown to be more efficient.
 4
Fully connected layer:
Adding a fully connected layer to a CNN system is another good way to learn nonlinear combinations of the features coming from the previous layer. However, the fully connected layer takes all the neurons from the previous layer and connects them to each neuron it has. The output of this layer is given by F(x)=σ(W_{l×k}×x) where k is the size of the input x, l is the number of neurons in the fully connected layer, and σ is the activation function. This results in a matrix W.
 5
Output layer:
This layer is a hot vector representing the class of the given input vector x and has a dimensionality equal to the number of classes. In this work, we have 44 classes. The resulting class is represented by C(x)={i  ∃i ∀ j≠i:x_{j}≤x_{i}}
 6
Softmax layer:
The error is propagated back over a Softmax layer. For an input vector x of dimension N, the Softmax layer calculates a mapping such that S(x):R^{N}→ [ 0, 1]^{N}, and for each component 1≤j≤N, the output is calculated as follows: \( S(x)_{j} = \frac {e^{x_{j}}} {\sum _{i=1}^{n}e^{x_{i}}} \)
In general, a CNN consists of several iterations of this succession of layers where the output of one layer is the input to the next layer. One of the advantages of CNNs is that they are trained quickly. The architecture of our CNN system is depicted in Fig. 2.
We used this CNN system as a baseline for comparison with the EMDHCNN system.
EMDHCNN system for performing dysarthric speech recognition
The proposed EMDHCNN system is depicted in Fig. 2. The dysarthric speech is first processed using the EMDH technique with the expected goal of enhancing its quality. In the second step, the Melfrequency cepstral coefficients (MFCCs) are extracted from the enhanced speech and used as the input features to a deep learningbased system using a convolutional neural network (CNN). The CNN performs the recognition of 44 dysarthric phonemes.
The classification of dysarthric phonemes is performed by a CNN composed of a convolutional layer using a 39 × 39 filter and 64 maps followed by a pooling layer with a 2 × 2 filter. A dropout of 0.2 is applied followed by a flattening layer and two fully connected layers of 500 neurons each. The last layer is the output layer with a size of 44, which corresponds to the number of phonemes to be recognized.
Experiments and results
The Nemours corpus was used throughout the experiments. This database is composed of 814 short nonsense sentences: 74 sentences each uttered by 11 American male speakers with different degrees of dysarthria [18]. This corpus was used to train the HMMGMM and CNN baseline systems. The enhanced dysarthric speech utterances were obtained after processing the original Nemours corpus with the EMDH technique. This new corpus was used to train the EMDHCNN system. Both the original corpus and the enhanced corpus were split into 70% and 30% to form training and test sets, respectively. We have also carried out experiments using 10fold crossvalidation to train and test speakerdependent systems and global systems.
Experimental setup
The CNN and HMMGMMbased systems were used as baseline references. Their input features are MFCCs extracted from the dysarthric speech without any preprocessing. For the EMDHCNN system, the MFCC features were extracted after preprocessing the corpus with the EMDH technique. Only the preprocessed MFCCs were used to train and test the EMDHCNN system. Python programming language and Keras library [19] with TensorFlow were used to carry out the experiments.
The systems were tested using input vectors composed of 26 MFCCs and their first derivatives (13 + 13 deltas), which was found to be the best configuration for this application. Prior crossvalidation experiments showed that the second derivatives do not improve the accuracy. Due to the difficulty of understanding dysarthric speech and the extreme variability observed between dysarthric speakers, the three systems were designed as speakerdependent. The best parameters of the CNN and EMDHCNN systems are depicted in Fig. 2.
The speakerdependent HMMGMM baseline system uses a monophone lefttoright model with Gaussian mixture output densities. Each phoneme is modeled by a 5state HMM model where the first and last states are nonemitting. The best HMMGMM system results were obtained with 4 Gaussian mixtures.
The use of a speakerdependent monophone model is justified by the lack of exhaustive training data. Indeed, in the context of dysarthria, there is a huge variability in the pronunciation of a single phoneme, and therefore, a large amount of training data is required to train a robust model. Numerous studies, such as one conducted to adapt dysarthric speech, suggest using speakerdependent monophone models as a baseline system [20].
Results and discussion
The accuracy of each system by speaker and the global accuracy of the three systems are shown in Tables 2 and 3, respectively. Table 2 clearly shows that the EMDH technique improves the accuracy of the CNN system for almost all speakers by an average of 2.07% and 4.90% using 70/30 split and 10fold, respectively. The same conclusion can be drawn for the overall accuracy, which is improved by 2.63% and 9.95% using 70/30 split and 10fold, respectively, as shown in Table 3.
The low performance obtained by the HMMGMM system was predictable because of the extreme variability of dysarthric speech and the relatively small amount of data available for training. In this context, the assumption made by the GMM, that the input occurrences conform to a Gaussian distribution, is probably not valid. It is important to mention that dysarthric speech is characterized by phonetic articulatory errors and other diverse and unfavorable artifacts, such as stuttering, accidental pauses, and involuntary breathing.
The obtained results show that the CNN has a good ability to extract latent features of dysarthric speech and can be trained more easily with a limited amount of data.
Conclusion and future work
In this paper, a new approach has been proposed to improve dysarthric speech recognition. We used empirical mode decomposition and Hurstbased mode selection to enhance dysarthric speech as a preprocessing step. The robust dysarthric speech recognition system was designed by coupling the EMDHbased enhancement process with a convolutional neural network. The obtained results show that the EMDHCNN system performed better than the HMMGMM and CNN baseline systems. While the results obtained are encouraging, it is still challenging to find accurate models because of the extreme variability and complexity of dysarthric speech. Future work will investigate the use of other robust acoustic features and a longterm, memorybased deep learning architecture.
Availability of data and materials
No data were generated during this study but are available from [18]. The code can be provided to reproduce the results.
Abbreviations
 CNN:

Convolutional neural network
 DNN:

Deep neural network
 EMD:

Empirical mode decomposition
 EMDH:

Empirical mode decomposition and Hurstbased mode selection
 HMMGMMs:

Hidden Markov with Gaussian Mixture Models
 IMFs:

Intrinsic mode functions
 MFCCs:

Melfrequency cepstral coefficients
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This work received financial support from the Natural Sciences and Engineering Research Council of Canada (15102018).
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The main contributions are from SM and SS. BA prepared the comparison between the three speech enhancement techniques, and ZB built the HMMGMM system. SS checked the entire article and polished the article structure. All authors read and approved the final manuscript.
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The first two authors have been active in the field for two decades.
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Sidi Yakoub, M., Selouani, S., Zaidi, B. et al. Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network. J AUDIO SPEECH MUSIC PROC. 2020, 1 (2020). https://doi.org/10.1186/s1363601901695
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Keywords
 Dysarthria
 Empirical mode decomposition
 Hurst mode selection
 Convolutional neural network