Voice Research Articles

Language and sensory characteristics are reflected in voice-evoked responses in low birth weight children.

Children born with very low birth weight (VLBW) are at higher risk for cognitive impairment, including language deficits and sensorimotor difficulties. Voice-evoked response (P1m), which has been suggested as a language development biomarker in young children, remains unexplored for its efficacy in VLBW children. Furthermore, the relation between P1m and sensory difficulties in VLBW children remains unclear. 40 children with VLBW were recruited at 5-to-6 years old (26 male, 14 female, mean age of months ± SD, 80.0 ± 4.9). We measured their voice-evoked brain response using child-customized magnetoencephalography (MEG) and examined the relation between P1m and language conceptual inference ability and sensory characteristics. The final sample comprised 36 children (23 boys, 13 girls; ages 61-86 months; gestational ages 24-36 weeks). As a result of multiple regression analysis, voice-evoked P1m in the left hemisphere was correlated significantly with language ability (β = 0.414 P = 0.015) and sensory hypersensitivity (β = 0.471 P = 0.005). Our findings indicate that the relation between P1m and language conceptual inference ability observed in term children in earlier studies is replicated in VLBW children, and suggests P1m intensity as a biomarker of sensory sensitivity characteristics. We investigated brain functions related to language development and sensory problems in very low birth-weight children. In very low birth weight children at early school age, brain responses to human voices are associated with language conceptual inference ability and sensory hypersensitivity. These findings promote a physiological understanding of both language development and sensory characteristics in very low birth weight children.

Emplacing Ecological Grief in Last Chance Tourism: Cryospheric Change and Travel in the Arctic

Last Chance Tourism (LCT) is an increasingly popular phenomenon whereby tourists seek encounters with vanishing landscapes, cultures, and endangered species. However, there are concerns that it is not sufficiently ecologically informed, has a large carbon footprint, and may put further pressure on vulnerable ecosystems and communities. This review specifically focuses on the Arctic, which is a major global frontier for LCT and is at the forefront of disruptive and accelerating climate change. It draws on theoretical insights from the Ecological Grief concept to chart a new research focus as well as a pathway to share empathy, concern, and sorrow between scientists, communities, and visitors. Key literature sources on LCT and Ecological Grief were selected from major international scientific journals and monographs. The major findings of the study are (i) the Arctic cryosphere is a life-sustaining entity and disruptive changes in its mechanisms currently threaten the unique ecologies and culture of the region and (ii) LCT must be attentive to the emotive accounts of loss and grief associated with cryospheric change and emplace both human and non-human voices in the narrative. These findings are relevant for LCT researchers, tourism planners, and conscious travelers in the Arctic who prioritize destination sustainability.

Web 3.0 security: Backdoor attacks in federated learning-based automatic speaker verification systems in the 6G era

With the advent of Next-Generation Web 3.0 and the integration of 6G technologies, digital industrial applications are undergoing unprecedented transformations. Among these, the field of intelligent voice recognition, particularly Federated Learning-based Automatic Speaker Verification (FL-ASV) systems, stands out by collaboratively training robust ASV models across systems while protecting sensitive voiceprint data. However, the aspect of security within such systems is still largely unexplored and presents potential vulnerabilities. To bridge this gap, we design a voiceprint-driven backdoor attack for FL-ASV, termed FedCTS. Concretely, we employ contrastive learning techniques to significantly improve the feature extraction process for individual speakers. This enhancement not only maintains the inherent performance of FL-ASV systems but also introduces a level of complexity that can mislead even the most skilled defenders. Furthermore, we intricately obfuscate the triggers by subtly embedding voice and backdoor clips within the utterances. This is achieved by dividing the utterances into chronological segments through a meticulously devised time-series injection strategy, thereby ensuring the triggers remain undetectable. Additionally, we have conceptualized a unique defense mechanism tailored to counter such attacks. This defense mechanism operates by scrutinizing the speaker’s frequencies and filtering out any suspicious frequencies that fall outside the normal range of human voice, helping to mitigate the risk of backdoor attacks without compromising the system’s functionality. In the context of the fast-evolving digitalized industrial landscape, our attack strategy, FedCTS, has demonstrated a significant improvement in effectiveness. It achieves an average increase of 7.03% in the attack success rate when compared to existing state-of-the-art methods.

Using Vocal-Based Emotions as a Human Error Prevention System with Convolutional Neural Networks

Human error is a mark assigned to an event that has negative effects or does not produce a desired result, with emotions playing an important role in how humans think and behave. If we detect feelings early, it may decrease human error. The human voice is one of the most powerful tools that can be used for emotion recognition. This study aims to reduce human error by building a system that detects positive or negative emotions of a user like (happy, sad, fear, and anger) through the analysis of the proposed vocal emotion component using Convolutional Neural Networks. By applying the proposed method to an emotional voice database (RAVDESS) using Librosa for voice processing and PyTorch, with the emotion classification of (happy/angry), the results show a better accuracy (98%) in comparison to the literature with regard to making a decision to deny or allow a user to access sensitive operations or send a warning to the system administrator prior to accessing system resources.

A population of neurons selective for human voice in the monkey brain

Many animals can extract useful information from the vocalizations of other species. Neuroimaging studies have evidenced areas sensitive to conspecific vocalizations in the cerebral cortex of primates, but how these areas process heterospecific vocalizations remains unclear. Using fMRI-guided electrophysiology, we recorded the spiking activity of individual neurons in the anterior temporal voice patches of two macaques while they listened to complex sounds including vocalizations from several species. In addition to cells selective for conspecific macaque vocalizations, we identified an unsuspected subpopulation of neurons with strong selectivity for human voice, not merely explained by spectral or temporal structure of the sounds. The auditory representational geometry implemented by these neurons was strongly related to that measured in the human voice areas with neuroimaging and only weakly to low-level acoustical structure. These findings provide new insights into the neural mechanisms involved in auditory expertise and the evolution of communication systems in primates.

Temporal Coherence Shapes Cortical Responses to Speech Mixtures in a Ferret Cocktail Party.

Segregation of complex sounds such as speech, music and animal vocalizations as they simultaneously emanate from multiple sources (referred to as the "cocktail party problem") is a remarkable ability that is common in humans and animals alike. The neural underpinnings of this process have been extensively studied behaviorally and physiologically in non-human animals primarily with simplified sounds (tones and noise sequences). In humans, segregation experiments utilizing more complex speech mixtures are common; but physiological experiments have relied on EEG/MEG/ECoG recordings that sample activity from thousands of neurons, often obscuring the detailed processes that give rise to the observed segregation. The present study combines the insights from animal single-unit physiology with segregation of speech-like mixtures. Ferrets were trained to attend to a female voice and detect a target word, both in presence or absence of a concurrent, equally salient male voice. Single neuron recordings were obtained from primary and secondary ferret auditory cortical fields, as well as frontal cortex. During task performance, representation of the female words became more enhanced relative to those of the (distractor) male in all cortical regions, especially in the higher auditory cortical field. Analysis of the temporal and spectral response characteristics during task performance reveals how speech segregation gradually emerges in the auditory cortex. A computational model evaluated on the same voice mixtures replicates and extends these results to different attentional targets (attention to female or male voices). These findings are consistent with the temporal coherence theory whereby attention to a target voice anchors neural activity in cortical networks hence binding together channels that are coherently temporally-modulated with the target, and ultimately forming a common auditory stream.

Toward a systemic functional framework for the multimodal analysis of meaning reconstruction

AbstractIn recent decades, there has been a growing interest in multimodal translation studies. However, there is currently no suitable framework available for analyzing how the original meaning is reconstructed through the interaction of various semiotic resources in the multimodal target text. Under this context, this article proposes a systemic functional framework that integrates the systems of the meaning potential of semiotic resources, including language, sound (human voice), objects, space, and semiotic ensembles of the human body, and a tripartite classification of the relationships between semiotic resources. To exemplify the analytical potential of the framework, this article presents an example of analysis, taking an excerpt from Fences as the object of analysis.

The expressiveness of human speech and voice: on the 95th anniversary of the birth of V.P. Morozov

The article analyzes the professional path of Vladimir Petrovich Morozov, the author of fundamental works in the field of psychophysiology and psychoacoustics of speech and voice, non-verbal communication, scientific foundations of vocal art. The main directions of his scientific activity are highlighted, their connection with the trends in the development of modern science, high relevance and interdisciplinary demand are shown. The special style of Morozov’s scientific creativity is emphasized, manifested in a combination of original theoretical ideas with the solution of practical problems, addressing borderline issues of psychology and musical art. The most important milestones of the scientist’s professional path and achievements in the methodology and theory of speech and voice psychology, as well as in experimental research, are described.

The Evolution of Gender Dimorphism in the Human Voice

The Evolution of Gender Dimorphism in the Human Voice

Animal speech and singing synthesis model based on So-VITS-SVC

Currently, when researchers in deep learning and neural network technology have made significant progress, the author makes a new bold attempt to apply the technical principles of speech and singing synthesis with artificial intelligence to the field of animal speech and singing synthesis, using So-VITS-SVC4.0 framework, which was originally designed for human voice synthesis. Taking dogs as an example of a species and putting datasets of their sounds to use, the author is committed to capturing its sound characteristics and vocalization through model training and generating synthetic sounds with a high degree of similarity. This research may not only contribute to a deeper understanding of how animals communicate, but also open up new possibilities for animal sound art and music creation. With the continuous progress and improvement of technology, synthetic animal speech and singing by artificial intelligence may play an increasingly important role in zoological research and entertainment, bringing new perspectives and possibilities for communication between humans and animals.

Voice as a Biomarker of Pediatric Health: A Scoping Review.

The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of artificial intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0-17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were autism spectrum disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.

Voice Lessons

Abstract This paper reflects on the national referendum for an Indigenous Voice to Parliament that took place in Australia in mid-October 2023. At the time of writing, the aftershocks from the failure of the referendum to gain the necessary majorities were still being felt keenly by many of the Voice advocates and supporters. The hurt and grief of many First Nations people were shared by millions of non-Indigenous “Yes” voters, while much reckoning continued in the subsequent weeks and months. The author here explores what might have been gained if more attention had been given to what an Indigenous Voice to Parliament might “sound like,” instead of the excessive focus on, and public discourse around what it might “look like.” Resources from the philosophies and physiology of voice, communication ethics, cultural studies, critical anthropology, Australian Indigenous writing and scholarship, and psychoanalytic politics are utilised to explore the connections between the human voice, vocal expression, hearing and listening, silence and song.

Research on Intelligent Speech Interaction System Based on Residual Neural Network and Baidu Speech Platform

To address the dual needs of convenience and security in human-computer interaction under the context of the Internet of Things (IoT) and Artificial Intelligence (AI), a system has been designed based on Raspberry Pi4B, which integrates voice recognition, speech synthesis, and speaker verification functions. Voice recognition and speech synthesis capabilities leverage Baidu's speech platform technology, while speaker verification employs a Residual Neural Network (ResNet34) model based on the PyTorch framework. With a focus on enhancing the user experience, the system incorporates the snowboy offline voice wake-up engine for voice interaction and utilizes Python's Tkinter library to implement a customized graphical user interface (GUI). After strict testing and verification, this system not only efficiently and friendly meets various voice interaction scenarios in the field of Internet of Things technology, but also utilizes voiceprint recognition technology to ensure the application security of the system. It also provides research value for the innovation of open-source hardware platforms in the field of artificial intelligence.

Voice as a Biomarker in Health-Tech: Mapping the Evolving Landscape of Voice Biomarkers in the Start-Up World.

The vocal biomarkers market was worth $1.9B in 2021 and is projected to exceed $5.1B by 2028, for a compound annual growth rate of 15.15%. The investment growth demonstrates a blossoming interest in voice and artificial intelligence (AI) as it relates to human health. The objective of this study was to map the current landscape of start-ups utilizing voice as a biomarker in health-tech. A comprehensive search for start-ups was conducted using Google, LinkedIn, Twitter, and Facebook. A review of the research was performed using company website, PubMed, and Google Scholar. A 3-pronged approach was taken to thoroughly map the landscape. First, an internet search was conducted to identify current start-ups focusing on products relating to voice as a biomarker of health. Second, Crunchbase was utilized to collect financial and organizational information. Third, a review of the literature was conducted to analyze publications associated with the identified start-ups. A total of 27 start-up start-ups with a focus in the utilization of AI for developing biomarkers of health from the human voice were identified. Twenty-four of these start-ups garnered $178,808,039 in investments. The 27 start-ups published 194 publications combined, 128 (66%) of which were peer reviewed. There is growing enthusiasm surrounding voice as a biomarker in health-tech. Academic drive may complement commercialization to best achieve progress in this arena. More research is needed to accurately capture the entirety of the field, including larger industry players, academic institutions, and non-English content.

The Characterization of Normal Male and Female Voice from Surface Electromyographic Parameters.

Currently, there is no consensus on the characterization of the human voice. The objective of the present study is to describe the myoelectric behavior of the extrinsic musculature of the larynx in 146 people with normal voice (Spanish speakers), aged between 20 and 50 years old. Different vocal tasks were recorded using a surface electromyograph (SEMG). In all vocal tasks, it was observed that women had higher activation (µV) in the suprahyoid and sternocleidomastoid muscles than men, while men had higher activation in the infrahyoid muscles. SEMG is a valid procedure to help define normal vocal characteristics in the studied population, providing reference values during clinical examination. However, it is necessary to adopt a universal system of assessment tasks and standardized measurement techniques to allow for comparisons with future studies.

Human Voice Recognition for Authentication

Abstract: Human voice plays a very important role as a vital biometric parameter for authentication and Identification. Voice recognition is a biometric technology used to identify one particular person’s voice. It provides Enhanced security, convenient authentication and considerable cost saving. It can be performed using the concept of processing using Technique. Voice recognition is a software program or hardware device that can decode the human voice. It can receive and interpret dictation, understand and perform spoken commands, and identify a person based on their unique voiceprint. Voice recognition has become more popular in recent years with the rise of Artificial Intelligence (AI) and intelligent assistants, such as: Amazon’s Alexa, Apple’s Siri, Google Assistant. Voice recognition is used to control a smart home ,Instruct a smart speaker, Command phones and tablets ,Voice recognition is also known as Automatic speech recognition (ASR), Computer speech recognition.

Spectrum estimation for voiced speech using average weighted linear prediction

In many different areas, such as neurophysics, geophysics, and communication, it is vital to effectively handle signals when there is unwanted disturbance. Linear prediction (LP) methods provide precise modeling capabilities for various parametric models and are used in a wide range of fields such as predicting future data, compressing speech, analyzing spectra, filling in missing signals, and decreasing noise. LP is especially beneficial in speech processing systems. Speech analysis and synthesis greatly relies on spectral envelopes, which are closely connected to models of speech production and perception. However, the performance of the LP method may decline when limited information is involved in pitch synchronous analysis. In order to overcome this constraint, we developed the implementation of the Average Weighted Linear Prediction (AWLP) technique, which does not require the use of a window. The AWLP utilizes a signal of weighted prediction error acquired from the autocorrelation process of LP. By performing this action, it provides a more precise estimation of the power spectrum and coefficients used for vocal tract parametric models. Importantly, the effectiveness of the AWLP can be seen in both pitch synchronous and asynchronous analyses, which means it is appropriate for situations where segments are chosen randomly. We confirm the effectiveness of the AWLP technique by employing it on both synthetic voice and real speech. Additionally, we assess its effectiveness by comparing it to the direct autocorrelation technique and a modified autocorrelation (MA) approach. To sum up, the AWLP method presents a hopeful solution for reliable spectral analysis that addresses the divide between pitch synchronous and asynchronous situations.

Can we build a relationship through artificial intelligence (AI)? Understanding the impact of AI on organization-public relationships

The purpose of this study is to explore the role of agent type and voice tone on publics’ perceived organization-public relationships and behavioral intention towards an organization. Through a 2 (agent type: human vs. AI) x 2 (voice tone: conversational human vs. organizational) between-subjects experiment, the study shows that organizational messages communicated by a human agent generated significantly higher perceived control mutuality among participants in a Twitter setting than messages communicated by an AI agent. The use of conversational human voice resulted in significantly stronger relational outcomes. A significant interaction effect was found between agent type and voice tone on control mutuality. This study contributes to the public relations literature and practice by examining the potential role of AI agents in organization-public relationships and supportive behavioral intention towards an organization in the setting of Twitter.

Highly Sensitive Biomimetic Crack Pressure Sensor with Selective Frequency Response.

High-sensitivity sensors in practical applications face the issue of environmental noise interference, requiring additional noise reduction circuits or filtering algorithms to improve the signal-to-noise ratio (SNR). To address this issue, this study proposes a biomimetic crack pressure sensor with selective frequency response based on hydrogel dampers. The core of this research is to construct a biomimetic crack pressure sensor with selective frequency response using the high-pass filtering characteristics of gelatin-chitosan hydrogels. This design, inspired by the slit sensilla and stratum corneum structure of spider legs, delves into the material properties and principles of hydrogel dampers, exploring their application in biomimetic crack pressure sensors, including parameter selection, structural design, and performance optimization. By delving into the nuanced characteristics and working principles of hydrogel dampers, their integration in biomimetic crack pressure sensors is examined, focusing on aspects like parameter selection, structural engineering, and performance enhancement to selectively sieve out low-frequency noise and transmit target vibrational signals. Experimental results demonstrate that this innovative sensor, while suppressing low-frequency vibration signals, can selectively detect high-frequency signals with high sensitivity. At different vibration frequencies, the relative change in resistance exceeds 200%, and the sensor sensitivity is 7 × 104 kPa-1. Furthermore, this sensor was applied to human voice detection, and the corresponding results verified its frequency-selective performance evidently. This study not only proposes a new design for pressure sensors but also offers fresh insights into the application of biomimetic crack pressure sensors in intricate environments.

Vehicle Control by using Human Voice for Physically Disabled and Aged People

The most crucial form of human communication is speech signals. Almost all encounters use speech signals in conversations. With a microphone, sounds and different speech signals can be transformed into electrical form. Physical disability can result from a variety of causes, such as age-related issues, health issues, and accident-related injuries. For these challenged individuals who suffer from impairments in their hands and legs, vehicles are used as a means of transportation. It might be challenging for anyone with certain conditions, such as paralysis, to manually push a wheelchair or use a remote assembly. For these individuals, the project is made to work with voice-based commands, enabling the paralyzed or impaired person to offer directions simply by speaking into the provided microphone. Additionally, the device has directional buttons for remote vehicle control. An Atmega328-based circuit interfaced with a voice recognition module builds up the system. The voice recognition module receives user commands and translates them into digital data, which the micro-controller analyzes to obtain directional orders. The transmitter circuit and the receiver circuit make up the whole system. The voice recognition module is part of the transmitter circuit, and the motor and driver assembly are part of the one that receives circuit. For communication, an NRF trans-receiver module is used. The command issued to the vehicle is shown on a 16*2 LCD. Keywords: Bluetooth module, Arduino, wireless night vision camera, robotic arm.