In this work, we leverage on a novel distributional loss to improve vector-to-vector regression for feature-based speech enhancement (SE). The distributional loss function is devised based on the ...Kullback-Leibler divergence between a selected target distribution and a conditional distribution to be learned from the data for each coefficient in the clean speech vector given the noisy input features. A deep model having a softmax layer per coefficient is employed to parametrize the conditional distribution, and deep model parameters are found by minimizing a weighted sum of the cross-entropy between its outputs and respective target distributions. Experiments with convolutional neural networks (CNNs) on publicly available noisy speech dataset obtained from the Voice Bank corpus show consistent improvement over conventional solutions based on the mean squared error (MSE), and the least absolute deviation (LAD). Moreover, our approach compares favourably in terms of both speech quality and intelligibility against the Mixture Density Networks (MDNs), which is also an approach that relies on computing parametric conditional distributions based on Gaussian mixture models (GMMs) and a neural architecture. Comparison against GAN-based solutions are presented as well.
Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech ...spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.
In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) ...presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression. First, we show that a generalized upper-bound for DNN-based vector-to-vector regression can be ensured by leveraging the known Lipschitz continuity property of MAE. Next, we derive a new generalized upper bound in the presence of additive noise. Finally, in contrast to conventional MSE commonly adopted to approximate Gaussian errors for regression, we show that MAE can be interpreted as an error modeled by Laplacian distribution. Speech enhancement experiments are conducted to corroborate our proposed theorems and validate the performance advantages of MAE over MSE for DNN based regression.
In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors ...is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization theory, we derive upper bounds for each of the three aforementioned errors and impose necessary constraints on DNN models. Moreover, we assess our theoretical results through a set of image de-noising and speech enhancement experiments. Our proposed upper bounds of MAE for DNN based vector-to-vector regression are corroborated by the experimental results and the upper bounds are valid with and without the "over-parametrization" technique.
Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches ...promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer’s disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme–grapheme correspondence, e.g., cat), non-regular (have atypical phoneme–grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme–grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.
•We investigated the handwriting of people affected by Alzheimer’s disease (AD).•We investigated the impact of word semantics and phonology.•Investigating handwriting in AD patients holds significant clinical relevance.•Dysgraphia can serve as an additional indicator for timely diagnosis.•Results confirmed AD harms brain areas processing visual feedback.
This paper focuses on a theoretical analysis of deep neural network (DNN) based functional approximation. Leveraging upon two classical theorems on universal approximation, an artificial neural ...network (ANN) with a single hidden layer of neurons is used. With modified ReLU and Sigmoid activation functions, we first generalize the related concepts to vector-to-vector regression. Then, we show that the width of the hidden layer of ANN is numerically related to the approximation of the regression function. Furthermore, we increase the number of hidden layers and show that the depth of the ANN-based regression function can enhance its expressive power. We illustrate this representation with recently-emerged DNN based speech enhancement. We first compare the expressive power by varying ANN structures and then test its related regression performance under different noisy conditions in various noise types and signal-to-noise-ratio levels. Experimental results verify our theoretical prediction that an ANN of a broader hidden layer and a deeper architecture can jointly ensure a closer approximation of the vector-to-vector regression functions in terms of the Euclidean distance between the log power spectra of noisy and expected clean speech. Moreover, a DNN with a broader width at the top hidden layer can improve the regression performance relative to those with a narrower width at the top hidden layers.
In this paper, we investigate the effectiveness of articulatory information for Mandarin tone modeling and recognition in a deep neural network – hidden Markov model (DNN-HMM) framework. In ...conventional approaches, prosodic evidence (e.g., F0, duration and energy) is used to build tone classifiers, we here propose performance enhancement techniques in three areas: (i) adding articulatory features (AFs) and acoustic features, such as MFCCs (Mel frequency cepstrum coefficients), for tone modeling; (ii) adopting phone-dependent tone modeling; and (iii) using tone-based extended recognition network (ERN) to reduce the tone search space. The first approach is feature-related, it explicitly employs the AFs as a form of tonal features and is implemented through a multi-stage procedure. The second approach is model-related and directly extends to phone-dependent tone modeling so that each modeling unit (e.g., tonal phone) not only contains tone information, but also integrates the phone/articulatory information. Finally, the third technique is search-related with a phone-dependent tone-based expanding searching network. A series of comprehensive experiments is conducted using different input feature sets. It is demonstrated that (i) tone recognition accuracy is boosted by incorporating articulatory information, and (ii) ERN, attains the lowest tone error rate of 7.17%, with a 56% relative error reduction from the prosody-only baseline system error of 16.36%.
The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems ...can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade‐off‐based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina‐iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits.
Data-driven deep learning solutions with gradient-based neural architecture, have proven useful in overcoming some limitations of traditional signal processing techniques. However, a large number of ...reverberant-anechoic training utterance pairs covering as many environmental conditions as possible is required to achieve robust dereverberation performance in unseen testing conditions. In this article, we propose to address the data requirement issue while preserving the advantages of deep neural structures leveraging upon hierarchical extreme learning machines (HELMs), which are not gradient-based neural architectures. In particular, an ensemble HELM learning framework is established to effectively recover anechoic speech from a reverberant one based on spectral mapping. In addition to the ensemble learning framework, we further derive two novel HELM models, namely, highway HELM HELM(Hwy) and residual HELM HELM(Res), both incorporating low-level features to enrich the information for spectral mapping. We evaluated the proposed ensemble learning framework using simulated and measured impulse responses by employing Texas Instrument and Massachusetts Institute of Technology (TIMIT), Mandarin hearing in noise test (MHINT), and reverberant voice enhancement and recognition benchmark (REVERB) corpora. The experimental results show that the proposed framework outperforms both traditional methods and a recently proposed integrated deep and ensemble learning algorithm in terms of standardized objective and subjective evaluations under matched and mismatched testing conditions for simulated and measured impulse responses.
Cyber attacks and anomaly detection are problems where the data is often highly unbalanced towards normal observations. Furthermore, the anomalies observed in real applications may be significantly ...different from the ones contained in the training data. It is, therefore, desirable to study methods that are able to detect anomalies only based on the distribution of the normal data. To address this problem, we propose a novel objective function for generative adversarial networks (GANs), referred to as STEP-GAN. STEP-GAN simulates the distribution of possible anomalies by learning a modified version of the distribution of the task-specific normal data. It leverages multiple generators in a step-by-step interaction with a discriminator in order to capture different modes in the data distribution. The discriminator is optimized to distinguish not only between normal data and anomalies but also between the different generators, thus encouraging each generator to model a different mode in the distribution. This reduces the well-known mode collapse problem in GAN models considerably. We tested our method in the areas of power systems and network traffic control systems (NTCSs) using two publicly available highly imbalanced datasets, ICS (Industrial Control System) security dataset and UNSW-NB15, respectively. In both application domains, STEP-GAN outperforms the state-of-the-art systems as well as the two baseline systems we implemented as a comparison. In order to assess the generality of our model, additional experiments were carried out on seven real-world numerical datasets for anomaly detection in a variety of domains. In all datasets, the number of normal samples is significantly more than that of abnormal samples. Experimental results show that STEP-GAN outperforms several semi-supervised methods while being competitive with supervised methods.
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