Voice recognition

Personal human identification is a crucial aspect of modern society with applications spanning from law enforcement to healthcare and digital security. This bibliometric paper presents a comprehensive analysis of recent advances in personal human identification methodologies focusing on biomedical traits. The paper examines a diverse range of research articles, reviews, and patents published over the last decade to provide insights into the evolving landscape of biometric identification techniques. The study categorizes the identified literature into distinct biomedical trait categories, including but not limited to, fingerprint and palmprint recognition, iris and retinal scanning, facial recognition, voice and speech analysis, gait recognition, and DNA-based identification...

The many stimulating contributions to this Special Issue of Brain Science focused on some basic issues of particular interest in current research, with emphasis on human recognition using faces, voices, and names [...].

Digital speech recognition is a challenging problem that requires the ability to learn complex signal characteristics such as frequency, pitch, intensity, timbre, and melody, which traditional methods often face issues in recognizing. This article introduces three solutions based on convolutional neural networks (CNN) to solve the problem: 1D-CNN is designed to learn directly from digital data; 2DS-CNN and 2DM-CNN have a more complex architecture, transferring raw waveform into transformed images using Fourier transform to learn essential features...

BACKGROUND: With an international nursing shortage, there is a need to navigate towards an improved nursing workforce structure where each nursing role is valued and recognised for the work they contribute. The second-level regulated nursing role is seen as integral; however, there is role confusion, especially with the registered nurse, and high attrition. To implement strategies to retain an integral nursing workforce, there is a need to better understand the role from the experiences and expectations of the second-level regulated nursing role...

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes...

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence...

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions...

PURPOSE: We studied the role of gender in metacognition of voice emotion recognition ability (ERA), reflected by self-rated confidence (SRC). To this end, we guided our study in two approaches: first, by examining the role of gender in voice ERA and SRC independently and second, by looking for gender effects on the ERA association with SRC. METHOD: We asked 100 participants (50 men, 50 women) to interpret a set of vocal expressions portrayed by 30 actors (16 men, 14 women) as defined by their emotional meaning...

INTRODUCTION: Speech Sound Disorder (SSD) is a speech and language disorder associated with difficulties in motor production, perception, and phonological representation of sounds and speech segments. Since auditory perception has a fundamental role in forming and organizing sound representation for its recognition, studies that evaluate the cortical processing of sounds are required. Thus, the present study aimed to verify the relation between SSD severity measured by the Percentage of Correct Consonants (PCC) with the cortical auditory evoked potentials (CAEP) using speech stimulus...

Flexible electronics have gained a lot of attention in recent years due to their compatibility with soft robotics, artificial arms, and many other applications. Meanwhile, the detection of acoustic frequencies is a very useful tool for applications ranging from voice recognition to machine condition monitoring. In this work, the dynamic response of Pt nanoparticles (Pt NPs)-based strain sensors on flexible substrates is investigated. the nanoparticles were grown in a vacuum by magnetron-sputtering inert-gas condensation...

In recent years, attention to the realization of a distributed fiber-optic microphone for the detection and recognition of the human voice has increased, whereby the most popular schemes are based on φ-OTDR. Many issues related to the selection of optimal system parameters and the recognition of registered signals, however, are still unresolved. In this research, we conducted theoretical studies of these issues based on the φ-OTDR mathematical model and verified them with experiments. We designed an algorithm for fiber sensor signal processing, applied a testing kit, and designed a method for the quantitative evaluation of our obtained results...

The ongoing biodiversity crisis, driven by factors such as land-use change and global warming, emphasizes the need for effective ecological monitoring methods. Acoustic monitoring of biodiversity has emerged as an important monitoring tool. Detecting human voices in soundscape monitoring projects is useful both for analyzing human disturbance and for privacy filtering. Despite significant strides in deep learning in recent years, the deployment of large neural networks on compact devices poses challenges due to memory and latency constraints...

INTRODUCTION: The growing recognition of holistic patient care highlights the various factors shaping the quality of life of individuals with autoimmune and rheumatic diseases (AIRDs). Beyond the traditional disease measures, there is an emerging acknowledgment of the less-explored aspects, including subjective well-being, social determinants of health, comorbidities, mental health, and medication adherence. Moreover, digital health services have empowered patients to engage actively in decision-making alongside clinicians...

Flexible pressure sensors (FPSs) based on biomass materials have gained considerable attention for their potential in wearable electronics, human-machine interaction, and environmental protection. Herein, flexible silver nanowire-dual-cellulose paper (SNdCP) containing common cellulose fibers, cellulose nanofibers (CNFs), and silver nanowires (AgNWs) for FPSs was assembled by a facile papermaking strategy. Compared with bacterial cellulose (BC) and cellulose nanocrystals (CNCs), CNFs possess better dimensions and reinforcement, which enables the composite paper to exhibit better mechanical properties (tensile stress of 164...

INTRODUCTION: Emotional recognition from audio recordings is a rapidly advancing field, with significant implications for artificial intelligence and human-computer interaction. This study introduces a novel method for detecting emotions from short, 1.5 s audio samples, aiming to improve accuracy and efficiency in emotion recognition technologies. METHODS: We utilized 1,510 unique audio samples from two databases in German and English to train our models. We extracted various features for emotion prediction, employing Deep Neural Networks (DNN) for general feature analysis, Convolutional Neural Networks (CNN) for spectrogram analysis, and a hybrid model combining both approaches (C-DNN)...

Prosopagnosia, also referred to as "face blindness," is a type of visual agnosia characterized by a decreased capacity to recognize familiar faces with a preserved ability to identify individuals based on non-facial visual traits or voice. Prosopagnosia can be categorized as developmental (DP) or acquired (AP) owing to a variety of underlying conditions, including trauma, neurodegenerative diseases, stroke, neuroinfections, and, less frequently, malignancies. Facial recognition is a complex process in which different neuronal networks are involved...

BACKGROUND: Verbatim transcription of qualitative audio data is a cornerstone of analytic quality and rigor, yet the time and energy required for such transcription can drain resources, delay analysis, and hinder the timely dissemination of qualitative insights. In recent years, software programs have presented a promising mechanism to accelerate transcription, but the broad application of such programs has been constrained due to expensive licensing or "per-minute" fees, data protection concerns, and limited availability of such programs in many languages...

INTRODUCTION: The practice of sonography in the United Arab Emirates is evolving rapidly. This study explores the professional landscape and challenges of sonography practice in the United Arab Emirates, focusing on identifying areas for improvement in education, practice scope, and professional development. METHODS: Employing qualitative methodologies, using focus group discussions and thematic analysis, this research gathers insights from practicing sonographers across various healthcare settings in the UAE to assess current practices, educational needs, and workplace challenges...

The Voiceitt app is designed for people with dysarthric speech, to support vocal communication and access to voice-driven technologies. Sixty-six participants were recruited to test the Voiceitt app and share feedback with developers. Most had physical, sensory, or cognitive impairments in addition to atypical speech. The project team liaised with individuals, their families and local support teams to provide access to the app and associated equipment. Testing was user-led, with participants asked to identify and test use cases most relevant to their daily lives over three months or more...

OBJECTIVES: We provide a novel application of psycholinguistic theories and methods to the field of auditory training to provide preliminary data regarding which minimal pair contrasts are more difficult for listeners with typical hearing to distinguish in real-time. DESIGN: Using eye-tracking, participants heard a word and selected the corresponding image from a display of four: the target word, two unrelated words, and a word from one of four contrast categories (i...