The integration of renewable energy sources into the electrical grid introduces complex challenges in fault detection and coordination of grid recovery mechanisms. Traditional relay protection systems, which operate based on static rules and predefined thresholds, are inadequate for addressing these challenges, particularly in detecting and isolating faults such as short circuits. Consequently, the conventional methodologies applied to electrical network protection frequently fail to achieve optimal performance in fault detection, especially in terms of adherence to safety standards and the selective limitation of damage. Recent research indicates that machine learning (ML)-based approaches can effectively tackle these issues; however, variations in grid configurations and analysis windows have impeded consistent comparative assessments. In this study, we assess the efficacy of various ML models in detecting electrical faults and pinpointing defective transmission lines within a 10 ms measurement interval—a critical time-frame for real-time operational viability, for the first time. The most effective model attained an F1 score of 0.991±0.018 and demonstrated a processing time of 0.342ms±0.509ms.

Hypertension is considered to be one of the most critical risk factors for cardiovascular diseases. As such, continuous, accurate and non-invasive monitoring of blood pressure (BP) is of utmost importance and research on such approaches is gaining momentum. In this study, we propose a novel transformer-based model architecture that leverages historic pressure wave information for accurate blood pressure regression. We achieve remarkable results that satisfy both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) blood pressure monitoring standards, with 97% of errors less than 5mmHg and 1.02 ± 1.77mmHg (mean absolute error ± standard deviation) accuracy for systolic (SBP), and 93% and 1.57 ± 2.36mmHg for diastolic BP (DBP). To the best of our knowledge, this is the first approach that utilizes transformers for BP regression and the first radar approach to satisfy BP standards, demonstrating the predictive power of the proposed model and the suitability of radar for the task.

Magnetic Resonance Imaging (MRI) allows analyzing speech production by capturing high-resolution images of the dynamic processes in the vocal tract. In clinical applications, combining MRI with synchronized speech recordings leads to improved patient outcomes, especially if a phonological-based approach is used for assessment. However, when audio signals are unavailable, the recognition accuracy of sounds is decreased when using only MRI data. We propose a contrastive learning approach to improve the detection of phonological classes from MRI data when acoustic signals are not available at inference time. We demonstrate that frame-wise recognition of phonological classes improves from an f1 of 0.74 to 0.85 when the contrastive loss approach is implemented. Furthermore, we show the utility of our approach in the clinical application of using such phonological classes to assess speech disorders in patients with tongue cancer, yielding promising results in the recognition task.

Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model’s decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition; then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD.

Current state of the art acoustic models can easily comprise more than 100 million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function. An ideal dataset is not necessarily large in size, but large with respect to the amount of unique speakers, utilized hardware and varying recording conditions. This enables a machine learning model to explore as much of the domain-specific input space as possible during parameter estimation. This work introduces Common Phone, a gender-balanced, multilingual corpus recorded from more than 76.000 contributors via Mozilla’s Common Voice project. It comprises around 116 hours of speech enriched with automatically generated phonetic segmentation. A Wav2Vec 2.0 acoustic model was trained with the Common Phone to perform phonetic symbol recognition and validate the quality of the generated phonetic annotation. The architecture achieved a PER of 18.1 % on the entire test set, computed with all 101 unique phonetic symbols, showing slight differences between the individual languages. We conclude that Common Phone provides sufficient variability and reliable phonetic annotation to help bridging the gap between research and application of acoustic models.

The PSEN1-E280A or Paisa mutation is responsible for most of Early-Onset Alzheimer’s (EOA) disease cases in Colombia. It affects a large kindred of over 5000 members that present the same phenotype. The most common symptoms are related to language disorders, where speech fluency is also affected due to the difficulty to access semantic information intentionally. This study proposes the use of acoustic and linguistic methods to extract features from speech recordings and their transcriptions to discriminate people with conditions related to the Paisa mutation. We consider state-of-the-art word-embedding methods like Word2Vec and Bidirectional Encoder Representations from Transformer to process the transcripts. The speech signals are modeled by using traditional acoustic features and speaker embeddings. To the best of our knowledge, this is the first study focused on evaluating genetic Alzheimer’s and EOA using acoustics and linguistics.

Parkinson’s disease is a neurodegenerative disorder characterized by the presence of different motor impairments. Speech and gait signals have been analyzed to detect the presence of the disease and the severity in patients. However, most studies have been performed in controlled conditions using high quality data, which make those studies not suitable for a continuous at-home evaluation of the state of the patients. The developed technology should be evaluated in more realistic scenarios, for instance using smartphone data. We propose the use of state-of-the-art deep learning techniques to evaluate the speech and gait symptoms of patients. The proposed methods are evaluated in two scenarios to cover both high quality and smartphone data. The results indicate that it is possible to classify patients and healthy subjects with accuracies over 92% in both scenarios. The proposed methods are also promising to evaluate the severity of the speech symptoms and the global motor state of the patients.

Against the background of the ongoing pandemic, this year’s Computational Paralinguistics Challenge featured a classification problem to detect Covid-19 from speech recordings. The presented approach is based on a phonetic analysis of speech samples, thus it enabled us not only to discriminate between Covid and non-Covid samples, but also to better understand how the condition influenced an individual’s speech signal. Our deep acoustic model was trained with datasets collected exclusively from healthy speakers. It served as a tool for segmentation and feature extraction on the samples from the challenge dataset. Distinct patterns were found in the embeddings of phonetic classes that have their place of articulation deep inside the vocal tract. We observed profound differences in classification results for development and test splits, similar to the baseline method. We concluded that, based on our phonetic findings, it was safe to assume that our classifier was able to reliably detect a pathological condition located in the respiratory tract. However, we found no evidence to claim that the system was able to discriminate between Covid-19 and other respiratory diseases.

Alzheimer’s Disease (AD) results from the progressive loss of neurons in the hippocampus, which affects the capability to produce coherent language. It affects lexical, grammatical, and semantic processes as well as speech fluency. This paper considers the analyses of speech and language for the assessment of AD in the context of the Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSSo) 2021 challenge. We propose to extract acoustic features such as X-vectors, prosody, and emotional embeddings as well as linguistic features such as perplexity, and word-embeddings. The data consist of speech recordings from AD patients and healthy controls. The transcriptions are obtained using a commercial automatic speech recognition system. We outperform baseline results on the test set, both for the classification and the Mini-Mental State Examination (MMSE) prediction. We achieved a classification accuracy of 80% and an RMSE of 4.56 in the regression. Additionally, we found strong evidence for the influence of the interviewer on classification results. In cross-validation on the training set, we get classification results of 85% accuracy using the combined speech of the interviewer and the participant. Using interviewer speech only we still get an accuracy of 78%. Thus, we provide strong evidence for interviewer influence on classification results.

Abstract Time–frequency representations of the speech signals provide dynamic information about how the frequency component changes with time. In order to process this information, deep learning models with convolution layers can be used to obtain feature maps. In many speech processing applications, the time–frequency representations are obtained by applying the short-time Fourier transform and using single-channel input tensors to feed the models. However, this may limit the potential of convolutional networks to learn different representations of the audio signal. In this paper, we propose a methodology to combine three different time–frequency representations of the signals by computing continuous wavelet transform, Mel-spectrograms, and Gammatone spectrograms and combining then into 3D-channel spectrograms to analyze speech in two different applications: (1) automatic detection of speech deficits in cochlear implant users and (2) phoneme class recognition to extract phone-attribute features. For this, two different deep learning-based models are considered: convolutional neural networks and recurrent neural networks with convolution layers.

People with pre- and postlingual onset of deafness, i.e, age of occurrence of hearing loss, often present speech production\nproblems even after hearing rehabilitation by cochlear implantation. In this paper, the speech of 20 prelinguals (aged between 18 to 71 years old), 20 postlinguals (aged between 33 to 78 years old) and 20 healthy control (aged between 31 to 62 years old) German native speakers are analyzed considering phone-attribute features extracted with pre-trained Deep Neural Networks. Speech signals are analyzed with reference to the manner of articulation of consonants according to 5 groups: nasals, sibilants, fricatives, voiced-stops, and voiceless-stops. According to the results, it is possible to detect alterations in the consonant production of CI users when compared with healthy speakers. A comprehensive evaluation of speech changes of CI users will help in the rehabilitation after deafening.

Parkinson's disease is a neurodegenerative disorder characterized by a variety of motor symptoms. Particularly, difficulties to start/stop movements have been observed in patients. From a technical/diagnostic point of view, these movement changes can be assessed by modeling the transitions between voiced and unvoiced segments in speech, the movement when the patient starts or stops a new stroke in handwriting, or the movement when the patient starts or stops the walking process. This study proposes a methodology to model such difficulties to start or to stop movements considering information from speech, handwriting, and gait. We used those transitions to train convolutional neural networks to classify patients and healthy subjects. The neurological state of the patients was also evaluated according to different stages of the disease (initial, intermediate, and advanced). In addition, we evaluated the robustness of the proposed approach when considering speech signals in three different languages: Spanish, German, and Czech. According to the results, the fusion of information from the three modalities is highly accurate to classify patients and healthy subjects, and it shows to be suitable to assess the neurological state of the patients in several stages of the disease. We also aimed to interpret the feature maps obtained from the deep learning architectures with respect to the presence or absence of the disease and the neurological state of the patients. As far as we know, this is one of the first works that considers multimodal information to assess Parkinson's disease following a deep learning approach.

Parkinson's disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in noncontrolled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to improve the quality of the signals. Two different classification approaches are considered: the classification of PD patients and healthy speakers and a multi-class experiment to classify patients in several stages of the disease. According to the results it is possible to classify PD patients and healthy controls with a AUe of up to 0.87. This work is a step forward to the development of telemonitoring systems to assess the speech of the patients.

Symptoms of Parkinson's disease vary from patient to patient. Additionally, the progression of those symptoms also differs among patients. Most of the studies on the analysis of speech of people with Parkinson's disease do not consider such an individual variation. This paper presents a methodology for the automatic and individual monitoring of speech disorders developed by PD patients. The neurological state and dysarthria level of the patients are evaluated. The proposed system is based on individual speaker models which are created for each patient. Two different models are evaluated, the classical GMM–UBM and the i–vectors approach. These two methods are compared with respect to a baseline found with a traditional Support Vector Regressor. Different speech aspects (phonation, articulation, and prosody) are considered to model recordings of spontaneous speech and a read text. A multi-aspect coefficient is proposed with the aim of incorporating information from all of these speech aspects into a single measure. Two different scenarios are considered to assess a set with seven PD patients: (1) the longitudinal test set which consists of speech recordings captured in five recording sessions distributed from 2012 to 2016, and (2) the at-home test set which consists of speech recordings captured in the home of the same seven patients during 4 months (one day per month, four times per day). The UBM is trained with the recordings of 100 speakers (50 with Parkinson's disease and 50 healthy speakers) captured with controlled acoustic conditions and a professional audio-setting. With the aim of evaluating the suitability of the proposed approaches and the possibility of extending this kind of systems to remotely assess the speech of the patients, a total of five different communication channels (sound-proof booth, Skype®, Hangouts®, mobile phone, and land-line) are considered to train and test the system. Due to the reduced number of recording sessions in the longitudinal test set, the experiments that involved this set are evaluated with the Pearson's correlation. The experiments with the at-home test set are evaluated with the Spearman's correlation. The results estimating the dysarthria level of the patients in the at-home test set indicate a correlation of 0.55 with a modified version of the Frenchay Dysarthria Assessment scale when the GMM-UBM model is applied upon the Skype® recordings. The results in the longitudinal test set indicate a correlation of 0.77 using a model based on i-vectors with recordings captured in the sound-proof-booth. The evaluation of the neurological state of the patients in the longitudinal test set shows correlations of up to 0.55 with the Movement Disorder Society - Unified Parkinson's Disease Rating Scale also using models based on i-vectors created with Skype® recordings. These results suggest that the i–vector approach is suitable when the acoustic conditions among recording sessions differ (longitudinal test set). The GMM-UBM approach seems to be more suitable when the acoustic conditions do not change a lot among recording sessions (at-home test set). Particularly, the best results were obtained with the Skype® calls, which can be explained due to several preprocessing stages that this codec applies to the audio signals. In general, the results suggest that the proposed approaches are suitable for tele-monitoring the dysarthria level and the neurological state of PD patients.