Voice recognition

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter...

OBJECTIVE: Auditory hallucinations (hearing voices) have been associated with a range of altered cognitive functions, pertaining to signal detection, source-monitoring, memory, inhibition and language processes. Yet, empirical results are inconsistent. Despite this, several theoretical models of auditory hallucinations persist, alongside increasing emphasis on the utility of a multidimensional framework. Thus, clarification of current evidence across the broad scope of proposed mechanisms is warranted...

Speech enhancement algorithms are applied in multiple levels of enhancement to improve the quality of speech signals under noisy environments known as multi-channel speech enhancement (MCSE) systems. Numerous existing algorithms are used to filter noise in speech enhancement systems, which are typically employed as a pre-processor to reduce noise and improve speech quality. They may, however, be limited in performing well under low signal-to-noise ratio (SNR) situations. The speech devices are exposed to all kinds of environmental noises which may go up to a high-level frequency of noises...

The discovery that listeners more accurately identify words repeated in the same voice than in a different voice has had an enormous influence on models of representation and speech perception. Widely replicated in English, we understand little about whether and how this effect generalizes across languages. In a continuous recognition memory study with Hindi speakers and listeners (N = 178), we replicated the talker-specificity effect for accuracy-based measures (hit rate and D'), and found the latency advantage to be marginal (p = 0...

Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML - eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application...

This study focused on investigating the potential of Artificial Intelligent-powered Virtual Assistants (VAs) such as Amazon Alexa, Apple Siri, and Google Assistant as tools to help individuals seeking information about Nicotine Replacement Treatment (NRT) for smoking cessation. The researchers asked 40 NRT-related questions to each of the 3 VAs and evaluated the responses for voice recognition. The study used a cross-sectional mixed-method design with a total sample size of 360 responses. Inter-rater reliability and differences between VAs' responses were examined by SAS software, and qualitative assessments were conducted using NVivo software...

The ability to reliably discriminate vocal expressions of emotion is crucial to engage in successful social interactions. This process is arguably more crucial for blind individuals, since they cannot extract social information from faces and bodies, and therefore chiefly rely on voices to infer the emotional state of their interlocutors. Blind have demonstrated superior abilities in several aspects of auditory perception, but research on their ability to discriminate vocal features is still scarce and has provided unclear results...

INTRODUCTION: Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline. METHODS: To evaluate multi-modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test-determined memory impairment...

Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2 ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design...

For the increasing number of patients with depression, this paper proposes an artificial intelligence method to effectively identify depression through voice signals, with the aim of improving the efficiency of diagnosis and treatment. Firstly, a pre-training model called wav2vec 2.0 is fine-tuned to encode and contextualize the speech, thereby obtaining high-quality voice features. This model is applied to the publicly available dataset - the distress analysis interview corpus-wizard of OZ (DAIC-WOZ). The results demonstrate a precision rate of 93...

INTRODUCTION: Over the past decade, several machine learning (ML) algorithms have been investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML algorithms can detect voice disorders with high accuracy. This suggests that ML has the potential to assist clinicians in the analysis and treatment outcome evaluation of voice disorders. However, despite numerous research studies, none of the algorithms have been sufficiently reliable to be used in clinical settings...

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers...

Speech recognition becomes increasingly important in the modern society, especially for human-machine interactions, but its deployment is still severely thwarted by the struggle of machines to recognize voiced commands in challenging real-life settings: oftentimes, ambient noise drowns the acoustic sound signals, and walls, face masks or other obstacles hide the mouth motion from optical sensors. To address these formidable challenges, an experimental prototype of a microwave speech recognizer empowered by programmable metasurface is presented here that can remotely recognize human voice commands and speaker identities even in noisy environments and if the speaker's mouth is hidden behind a wall or face mask...

Proposals to establish pan-Canadian licensure for physicians have broad support amongst medical groups to address physician shortages in underserved rural communities. The concept has also elicited concern from some stakeholders that its implementation could exacerbate rural physician workforce shortages by prompting an exodus of rural physicians to urban centres. An environmental scan of reports from key medical groups published within the past 10 years was conducted to determine factors influencing rural physician practice patterns...

BACKGROUND: The ability to differentiate familiar from unfamiliar humans has been considered a product of domestication or early experience. Few studies have focused on voice recognition in Felidae despite the fact that this family presents the rare opportunity to compare domesticated species to their wild counterparts and to examine the role of human rearing. METHODS: We tested whether non-domesticated Felidae species recognized familiar human voices by exposing them to audio playbacks of familiar and unfamiliar humans...

There is a critical need to develop and validate non-invasive animal-based indicators of affective states in livestock species, in order to integrate them into on-farm assessment protocols, potentially via the use of precision livestock farming (PLF) tools. One such promising approach is the use of vocal indicators. The acoustic structure of vocalizations and their functions were extensively studied in important livestock species, such as pigs, horses, poultry, and goats, yet cattle remain understudied in this context to date...

Emotion recognition shows large inter-individual variability, and is substantially affected by childhood trauma as well as modality, emotion portrayed, and intensity. While research suggests childhood trauma influences emotion recognition, it is unclear whether this effect is consistent when controlling for interrelated individual differences. Further, the universality of the effects has not been explored, most studies have not examined differing modalities or intensities. This study examined childhood trauma's association with accuracy, when controlling for alexithymia and psychopathy traits, and if this varied across modality, emotion portrayed, and intensity...

Children's participation and inclusion in violence research, particularly in low- and middle-income country (LMIC) contexts, is scant and not well understood. To assess how young children can be engaged in violence research, 4- to 7-year-old children were recruited into our pilot study in a rural area of South Africa. Six interviewers, recruited from the community, were trained to complete cognitive interviews ( n  = 24), interviewer-administered questionnaires ( n  = 21), and qualitative interviews ( n  = 18) with young children...

<b><br>Introduction:</b> Gastroesophageal Reflux Disease (GERD) is a common disorder in world population. As a result of the regurgitation of acid content from the stomach to laryngopharynx and larynx, secondary damage of laryngeal mucosa occur, which is highly sensitive to hydrochloric acid, and morphological changes are observed. Symptomatology of laryngopharyngeal reflux is varied which makes differential diagnosis difficult.</br> <b><br>Aim:</b> The aim of the study was the assessment of voice quality, morphological changes in larynx as well as etiology of Laryngopharyngeal Reflux Disease...

BACKGROUND: Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS...