Dysarthria is a speech disorder that affects the ability to communicate due to articulation difficulties. This research proposes a novel method for automatic dysarthria detection (ADD) and automatic dysarthria severity level assessment (ADSLA) by using a variable continuous wavelet transform (CWT) layered convolutional neural network (CNN) model. To determine their efficiency, the proposed model is assessed using two distinct corpora, TORGO and UA-Speech, comprising both dysarthria patients and healthy subject speech signals. The research study explores the effectiveness of CWT-layered CNN models that employ different wavelets such as Amor, Morse, and Bump. The study aims to analyze the models’ performance without the need for feature extraction, which could provide deeper insights into the effectiveness of the models in processing complex data. Also, raw waveform modeling preserves the original signal’s integrity and nuance, making it ideal for applications like speech recognition, signal processing, and image processing. Extensive analysis and experimentation have revealed that the Amor wavelet surpasses the Morse and Bump wavelets in accurately representing signal characteristics. The Amor wavelet outperforms the others in terms of signal reconstruction fidelity, noise suppression capabilities, and feature extraction accuracy. The proposed CWT-layered CNN model emphasizes the importance of selecting the appropriate wavelet for signal-processing tasks. The Amor wavelet is a reliable and precise choice for applications. The UA-Speech dataset is crucial for more accurate dysarthria classification. Advanced deep learning techniques can simplify early intervention measures and expedite the diagnosis process.

Automatic speech recognition (ASR) in children is a rapidly evolving field, as children become more accustomed to interacting with virtual assistants, such as Amazon Echo, Cortana, and other smart speakers, and it has advanced the human–computer interaction in recent generations. Furthermore, non-native children are observed to exhibit a diverse range of reading errors during second language (L2) acquisition, such as lexical disfluency, hesitations, intra-word switching, and word repetitions, which are not yet addressed, resulting in ASR’s struggle to recognize non-native children’s speech. The main objective of this study is to develop a non-native children’s speech recognition system on top of feature-space discriminative models, such as feature-space maximum mutual information (fMMI) and boosted feature-space maximum mutual information (fbMMI). Harnessing the collaborative power of speed perturbation-based data augmentation on the original children’s speech corpora yields an effective performance. The corpus focuses on different speaking styles of children, together with read speech and spontaneous speech, in order to investigate the impact of non-native children’s L2 speaking proficiency on speech recognition systems. The experiments revealed that feature-space MMI models with steadily increasing speed perturbation factors outperform traditional ASR baseline models.

Cardiovascular disease is a significant cause of death worldwide, emphasizing the crucial need for timely detection and diagnosis of heart abnormalities. This study presents a new approach that utilizes deep learning models to diagnose cardiac issues by analyzing raw phonocardiogram (PCG) signals. The proposed method introduces a novel technique called custom scalogram-based convolutional recurrent neural network (CS-CRNN). Diverging from conventional techniques, this model directly handles the raw PCG signals. These signals undergo a transformation into scalogram images within the initial layer of the CRNN architecture, without incorporating any learnable parameters. The results obtained from the CS-CRNN model are compared with traditional feature-based recurrent neural network (RNN) models. The comparison demonstrates comparable performance in both binary classification (normal and abnormal categories) and multiclass classification (5 categories). The CS-CRNN model directly handles raw PCG data and employs data augmentation to enhance performance on small datasets. It achieves an accuracy of 99.6% for binary classification and 98.6% and 99.7% before and after optimization for multiclass classification on the augmented dataset. The results show that the CS-CRNN model offers comparable performance to traditional methods, making it a promising tool for diagnosing cardiac abnormalities.