Voice Activity Research Articles

AN IN-DEPTH EXPLORATION OF ARTIFICIAL INTELLIGENCE IN THE CONTEXT OF CONTEMPORARY DATA CHALLENGES; DIFFERENCES BETWEEN HUMAN AND MACHINE LEARNING

Machine learning and artificial intelligence produce algorithms that appear to be able to make "intelligent" decisions similar to those of humans but function differently from human thinking. To make decisions based on machine suggestions, humans should be able to understand the background of these suggestions. However, since humans are oriented to understand human intelligence, it is not yet fully clear whether humans can truly understand the "thinking" generated by machine learning, or whether they merely transfer human-like cognitive processes to machines. In addition, media representations of artificial intelligence show higher capabilities and greater human likeness than they currently have. In our daily lives, we increasingly encounter assistance systems that are designed to facilitate human tasks and decisions based on intelligent algorithms. These algorithms are predominantly based on machine learning technologies, which make it possible to discover previously unknown correlations and patterns by analyzing large amounts of data. One example is the machine analysis of thousands of X-ray images of sick and healthy people. This requires identifying the patterns by which images labeled as "healthy" can be distinguished from those labeled as "sick" and to find an algorithm that identifies the latter. In the meantime, "trained" algorithms created in this way are used in various fields of application, not only for medical diagnoses but also in the pre-selection of applicants for a job advertisement or in communication with the help of voice assistants. These voice assistants are enabled by intelligent algorithms to offer internet services through short commands. Harald Lesch, referring to his book Unpredictable, written together with Thomas Schwarz, says the development of artificial intelligence can be compared to bringing aliens to Earth. With machine learning, a previously unknown form of non-human intelligence has been created. This chapter discusses whether forms of artificial intelligence, as they are currently being publicly discussed, differ substantially from human thinking. Furthermore, it will be discussed to what extent humans can comprehend the functioning of artificial intelligence that has been created through machine learning when interacting with them. Finally, the risks and opportunities will be weighed and discussed..

A Bibliometric Exploration of Voice Assistants in HR and Marketing

A Bibliometric Exploration of Voice Assistants in HR and Marketing

Do AI-Based Voice Assistants Influence Brand Continuous Usage? The Mediating Role of AI-Driven Customer Experience

This study aimed to assess the influence of artificial intelligence (AI) voice assistant (VA) on customer experience, resulting in the continuous use of mobile brands. Specifically, this article assesses the role of hedonic, utilitarian, and social benefits provided by AIVA on customer experience and continuous usage of a mobile phone brand. Using a primary data collection instrument, the quantitative approach was adopted to examine the study’s variables. Data from 348 valid responses were used for the analysis based on structural equation modeling (SEM) with AMOS version 23. Three main factors were identified to influence customer experience, which results in continuous usage of a mobile phone brand. These factors are social benefits, hedonic benefits, and utilitarian benefits. In conclusion, a significant and positive relationship exists between AI-enabled customer experience and brand continuous usage. It recommended that mobile brands consider and research their prospects’ and customers’ social, hedonic, and utilitarian needs to provide them with desired products and experiences.

Virtual Assistant Using Python

Today there is huge Advancement in the Technical field which is increasing day by day. In early days there were only computer systems where we were able to perform only few tasks, but today new technologies like machine learning, artificial intelligence, deep learning, and few some others have made computer systems so advance that we can perform any type of task with them. In recent years, Artificial Intelligence (AI) have done remarkable progress and its Capability is increasing day by day. One of the application Area of AI is Natural Language Processing (NLP). Natural Language Processing (NLP) helps Humans to communicate with the computer system in their own Language. For example, Voice Assistant. Various voice assistants were developed and they are still being improved more for better performance to overcome struggling of humans to interact with their machine. we are trying to develop a voice assistant using python which will help user to perform any type of task without interaction with keyboard. The aim of this paper is to study how voice assistants behaves smartly and can be used to get everyday work done and also be used for educational purpose also. Keywords: Virtual Assistant, UI, Artificial Intelligent, Python Library Key Features of the system are: ❖ Natural Language Understanding (NLU) ❖ Speech Recognition ❖ Text-to-Speech (TTS) Conversion ❖ User Interface ❖ Context Management

Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older Adults

Despite the plethora of telehealth applications to assist home-based older adults and healthcare providers, basic messaging and phone calls are still the most common communication methods, which suffer from limited availability, information loss, and process inefficiencies. One promising solution to facilitate patient-provider communication is to leverage large language models (LLMs) with their powerful natural conversation and summarization capability. However, there is a limited understanding of LLMs' role during the communication. We first conducted two interview studies with both older adults (N=10) and healthcare providers (N=9) to understand their needs and opportunities for LLMs in patient-provider asynchronous communication. Based on the insights, we built an LLM-powered communication system, Talk2Care, and designed interactive components for both groups: (1) For older adults, we leveraged the convenience and accessibility of voice assistants (VAs) and built an LLM-powered conversational interface for effective information collection. (2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system. The results showed that Talk2Care could facilitate the communication process, enrich the health information collected from older adults, and considerably save providers' efforts and time. We envision our work as an initial exploration of LLMs' capability in the intersection of healthcare and interpersonal communication.

Voice assistants and U.S. consumer behavior: A comprehensive review: investigating the role and influence of voice-activated technologies on shopping habits and brand loyalty

This study provides a comprehensive review of the influence of voice-activated technologies, particularly voice assistants, on consumer behavior and brand loyalty in the United States. The primary objective is to understand how these emerging technologies are reshaping shopping habits and the dynamics of brand-consumer interactions. The methodology encompasses a thorough analysis of peer-reviewed academic journals, industry reports, and case studies, supplemented by secondary sources such as market analyses and news articles. Key findings reveal that voice assistants have induced a significant paradigm shift in consumer behavior, characterized by changes in purchasing patterns, decision-making processes, and brand interactions. The convenience and personalized experience offered by these technologies have led to increased impulse buying and brand experimentation. However, this shift is accompanied by challenges in data privacy and security. The study also highlights the rapid evolution of voice-activated consumer engagement, with advancements in AI and machine learning enhancing personalization and efficiency. Strategically, the study recommends that brands and retailers invest in voice search optimization, develop secure data practices, and collaborate with technology developers to create engaging consumer experiences. The study concludes that while voice assistants present substantial opportunities for enhanced consumer engagement, they also pose challenges that require careful navigation, particularly in maintaining consumer trust and ethical standards. Future research directions include exploring the long-term effects of voice-activated shopping on consumer loyalty and the impact of demographic factors on the adoption of voice assistants. Keywords: Voice Assistants, Consumer Behavior, Brand Loyalty, Voice-Activated Technologies.

Wavoice: An mmWave-Assisted Noise-Resistant Speech Recognition System

As automatic speech recognition evolves, deployment of the voice user interface (VUI) has boomingly expanded. Especially since the COVID-19 pandemic, the VUI has gained more attention in online communication owing to its non-contact property. However, the VUI struggles to be applied in public scenes due to the degradation of received audio signals caused by various ambient noises. In this article, we propose Wavoice , the first noise-resistant multi-modal speech recognition system that fuses two distinct voices sensing modalities (i.e., millimeter-wave signals and audio signals from a microphone) together. One key contribution is to model the inherent correlation between millimeter-wave and audio signals. Based on it, Wavoice facilitates the real-time noise-resistant voice activity detection and user targeting from multiple speakers. Additionally, we elaborate on two novel modules for multi-modal fusion embedded into the neural network, leading to accurate speech recognition. Extensive experiments prove the effectiveness of Wavoice under adverse conditions—that is, the character recognition error rate below 1% in a range of 7 m. In terms of robustness and accuracy, Wavoice considerably outperforms existing audio-only speech recognition methods with lower character error and word error rates.

End-to-end integration of speech separation and voice activity detection for low-latency diarization of telephone conversations

Recent works show that speech separation guided diarization (SSGD) is an increasingly promising direction, mainly thanks to the recent progress in speech separation. It performs diarization by first separating the speakers and then applying voice activity detection (VAD) on each separated stream. In this work we conduct an in-depth study of SSGD in the conversational telephone speech (CTS) domain, focusing mainly on low-latency streaming diarization applications. We consider three state-of-the-art speech separation (SSep) algorithms and study their performance both in online and offline scenarios, considering non-causal and causal implementations as well as continuous SSep (CSS) windowed inference. We compare different SSGD algorithms on two widely used CTS datasets: CALLHOME and Fisher Corpus (Part 1 and 2) and evaluate both separation and diarization performance. To improve performance, a novel, causal and computationally efficient leakage removal algorithm is proposed, which significantly decreases false alarms. We also explore, for the first time, fully end-to-end SSGD integration between SSep and VAD modules. Crucially, this enables fine-tuning on real-world data for which oracle speakers sources are not available. In particular, our best model achieves 8.8% DER on CALLHOME, which outperforms the current state-of-the-art end-to-end neural diarization model, despite being trained on an order of magnitude less data and having significantly lower latency, i.e., 0.1 vs. 1 s. Finally, we also show that the separated signals can be readily used also for automatic speech recognition, reaching performance close to using oracle sources in some configurations.

Audio–visual speech recognition based on regulated transformer and spatio–temporal fusion strategy for driver assistive systems

This article presents a research methodology for audio–visual speech recognition (AVSR) in driver assistive systems. These systems necessitate ongoing interaction with drivers while driving through voice control for safety reasons. The article introduces a novel audio–visual speech command recognition transformer (AVCRFormer) specifically designed for robust AVSR. We propose (i) a multimodal fusion strategy based on spatio–temporal fusion of audio and video feature matrices, (ii) a regulated transformer based on iterative model refinement module with multiple encoders, (iii) a classifier ensemble strategy based on multiple decoders. The spatio–temporal fusion strategy preserves contextual information of both modalities and achieves their synchronization. An iterative model refinement module can bridge the gap between acoustic and visual data by leveraging their impact on speech recognition accuracy. The proposed multi-prediction strategy demonstrates superior performance compared to traditional single-prediction strategy, showcasing the model’s adaptability across diverse audio–visual contexts. The transformer proposed has achieved the highest values of speech command recognition accuracy, reaching 98.87% and 98.81% on the RUSAVIC and LRW corpora, respectively. This research has significant implications for advancing human–computer interaction. The capabilities of AVCRFormer extend beyond AVSR, making it a valuable contribution to the intersection of audio–visual processing and artificial intelligence.

AI EOS using Wolframe Alpha

Artificial intelligence is crucial to day-to-day life. science of computers defines AI research as the study of intelligent agents. Today, most people, intentionally or not, have turned to some form of computerized information processing technology. Artificial Intelligence (AI) is already changing our lifestyle. A device that senses its surroundings and performs actions that maximize the likelihood of achieving a goal. The input to a database can be a choose to of users and articles, and the output is a ruthless recommendation. Input can be verbally or textually submitted by the user within the system. This paper presents a new approach to intelligent search. Overall, there are many people around the world who use assistants. This paper describes the provocation of applying virtual assistant technology. The paper also introduces the application of virtual assistants that can help open opportunities for humanity in various fields. Voice control is an important growing feature that will change people's lives. Voice assistants are available for laptops, desktops, and mobile phones. Assistant is now available on all electronic devices. A voice assistant is a software agent that can interpret human speech and respond in machine language

Do AI-powered digital assistants influence customer emotions, engagement and loyalty? An empirical investigation

PurposeDigital voice assistants (DVAs) are revolutionising consumers’ interactions with technology and businesses. Whilst research on the adoption of these devices is rapidly expanding, few have explored post-adoption behaviour. To fill this gap, we investigate how functionality and human-like features shape customers’ emotions, engagement and loyalty towards DVAs.Design/methodology/approachThe data were collected through a self-administered online survey from 509 DVA users. Structural equation modelling was employed for data analysis.FindingsThe results reveal that distinct human-like and functional factors of DVA independently explain customers’ positive emotions and engagement with DVAs. Positive emotions and engagement significantly impact customer loyalty to DVAs. The study shows that localisation of DVAs has a significant positive moderating influence on the service experience-customer engagement relationship but a negative moderating influence on the anthropomorphism-customer engagement relationship.Originality/valueUnlike previous research, this study contributes to the literature by delving into post-adoption phenomena. It explains how DVAs’ human-like and functional attributes drive customers’ positive emotional responses, engagement and loyalty towards DVAs. The findings not only unveil new insights into the moderating role of localisation but also provide a crucial understanding regarding the boundary conditions of the influence of anthropomorphism and service experience on customer engagement.

Addressing Africa’s carbon dioxide emission: the role of natural resources, control of corruption, voice and accountability and regulatory quality

PurposeThe study examines the effect of natural resources (NRs) and the control of corruption, voice and accountability and regulatory quality on carbon emissions in Africa. Aside from their individual effects, the moderation effect of institutional quality is assessed.Design/methodology/approachData from 32 African countries from 2002 to 2021 and the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) regression methods were used for the investigation.FindingsIn the long term, the NRs effect is sensitive to the estimation technique employed. However, quality regulatory framework, robust corruption control and voice and accountability abate any positive effect of NRs on carbon emissions. Institutional quality can be argued to moderate the CO2-emitting potentials of resource extraction in the selected African countries.Practical implicationsEnhancing regulation quality, enforcing corruption control and empowering citizens towards greater participation in governance and demanding accountability are essential catalyst to effectively mitigate CO2 emissions resulting from NRs.Originality/valueThe moderation effect of control of corruption, voice and accountability and regulatory quality on the NR–carbon emission nexus is examined.

Love it or hate it? Deconstructing consumers' attitudes towards AI enabled voice shopping

AbstractThe conversational, social and intelligent capabilities of Artificial Intelligence (AI) enabled voice assistants (VAs) to assist humans have grown. However, their use still remains limited for complex tasks such as shopping. While some studies find that consumers are willing to use them, others report negative reactions that lead to rejection, especially for complex activities. This study aims to bridge the gap between the dichotomous streams of literature by investigating the overall attitude towards the use of AI VAs for a multi‐step task that is, shopping. We first identify 27 attitudinal criteria through a comprehensive literature review in light of the Uncanny Valley Theory and the Self Determination Theory. The criteria are organized using the cognitive, affective and conative framework of attitude, and ranked using the Fuzzy Analytical Hierarchy Process. Sensitivity analysis is done to affirm the robustness of the framework. Findings reveal that the behavioral intentions to use (conative criteria) are the strongest, followed by feelings evoked during use (affective criteria), followed by beliefs people hold regarding AI VAs (cognitive criteria). Intention to use AI voice assistants during all stages of the purchase journey takes precedence over negative feelings such as loss of control, unfulfillment and vulnerability. This study reconciles the existing stream of literature on conversational AI and provides managerial implications based on the dimensions of attitude.

Tax revenue mobilization and institutional quality in sub‐Saharan Africa: An empirical investigation

AbstractThis paper investigates the effects of institutional quality on tax revenue collection in sub‐Saharan African countries. We include the six institutional quality indicators (i.e., voice and accountability, political stability, regulatory quality, rule of law, control of corruption, and government effectiveness) as explanatory factors for tax revenues and its components in a sample of 42 countries over the period 1996–2019. A system GMM approach was used for the estimations. Consistent with previous results, we find that institutional quality has positive and significant effects on tax revenue collection in sub‐Saharan Africa. Specifically, aggregate total tax revenues, direct taxes, income taxes, and goods and services taxes are positively affected by the six institutional quality indicators. Indirect taxes are positively associated with four indicators, with the exception of government effectiveness and political stability, while tax on international trade are affected by institutional quality factors, with the exception of control of corruption and voice and accountability. These findings indicate that sub‐Saharan African countries should improve institutional quality to boost tax revenue collection.

The Role of Auditory Feedback in Voice Control in Normal and Impaired Hearing

The Role of Auditory Feedback in Voice Control in Normal and Impaired Hearing

Design of interactive online teaching system for big data computer information system based on mobile sensor network

The combination of the Internet and computers has resulted in many new products, including smart homes. The emergence of smart homes can satisfy people's detection of the internal environment of the home, not only to ensure the continuity and safety of indoor monitoring, but also to provide convenience for people to manage their family life. This paper analyzes the current status of smart home research at home and abroad, and people's needs for homes, and builds and designs smart home systems based on Ethernet and ZigBee protocol wireless sensor networks connected to TCP/IP protocol. In this article, the ARM chip is used in the Android system to ensure that the system and functions are matched, and information is transmitted through the serial port of the development board. After receiving the corresponding sending signal, the wireless module will use ZigBee technology to send the corresponding signal to achieve the goal of controlling home appliances. We divides the control system into two parts, the Android Voice Control Platform and the data collection terminal. The former is based on the Android operating system and is designed for voice recognition control; the latter uses a ZigBee microcontroller to achieve control and feedback. This article uses the built-in voice control system of the smart home to subtly replace the conventional control method of the smart home. Moreover, the dialogue with users is very high, easy to use, low cost, and very practical. In addition, the research methods used in this article can also be applied to other related fields.

Manipulator Control System Based on Gesture Recognition for Space Operation

In the scenario of on-orbit servicing, the complex space environment and the strict limitation of spacesuits made the manual control of manipulator extremely inconvenient. Current remote-control methods such as voice control, are commonly affected by the signal ratio, especially in the case of solar wind. Therefore, in this paper, we proposed a gesture-based remote-control system for real-time manipulation of the space manipulator in order to accomplish the onboard tasks. Gesture-based control strategy is more friendly on human-machine interaction than voice control or manual control. The proposed method: utilized gesture recognition technique to perform scheduled tasks according to the preset gestures. For preset missions, specific gestures are recognized and matched through KNN algorithm, and then manipulator is driven to execute the scheduled tasks. To validate the efficiency of the proposed method, extensive experiments are carried out on simulation platforms and compared with the traditional method of gesture recognition using rotation angle and gesture recognition using CNN. Results demonstrated that our method outperforms the other two solutions in terms of accuracy and speed.

Unlock Happy Interactions: Voice Assistants Enable Autonomy and Timeliness

This study examines the effects of three interactive voice assistant (VA) features (responsiveness, ubiquitous connectivity, and personalization) on consumer happiness. An online survey was administered to 316 VA consumers, and the data were analyzed using structural equation modeling with SmartPLS 4 software. The results indicate that VA responsiveness, ubiquitous connectivity, and personalization have significant effects on consumer happiness. This study also provides evidence that consumer happiness is influenced by VA features through the mediating roles of autonomy and timeliness. Notably, perceived privacy risk has a dual effect, negatively affecting happiness but positively moderating the relationship between autonomy and happiness, suggesting a complex interplay between benefits and concerns in user interactions with VAs. This study highlights the need for VA businesses to consider both the enhancing and mitigating factors of technology for user experiences. Furthermore, our findings have significant implications for VA businesses and executives, suggesting that improved interactions through these VA features can better serve consumers and enhance their experiences.

Speaker Anonymization: Disentangling Speaker Features from Pre-Trained Speech Embeddings for Voice Conversion

Speech is a crucial source of personal information, and the risk of attackers using such information increases day by day. Speaker privacy protection is crucial, and various approaches have been proposed to hide the speaker’s identity. One approach is voice anonymization, which aims to safeguard speaker identity while maintaining speech content through techniques such as voice conversion or spectral feature alteration. The significance of voice anonymization has grown due to the necessity to protect personal information in applications such as voice assistants, authentication, and customer support. Building upon the S3PRL-VC toolkit and on pre-trained speech and speaker representation models, this paper introduces a feature disentanglement approach to improve the de-identification performance of the state-of-the-art anonymization approaches based on voice conversion. The proposed approach achieves state-of-the-art speaker de-identification and causes minimal impact on the intelligibility of the signal after conversion.

A Review on Sensor Technologies and Control Methods for Mobile Robot with Obstacle Detection System

Obstacle detection system is a system that reacts to the object in the path and perform action such as stopping robot movement and collision prevention according to the design of algorithm which enhance the safety level of robot. This paper examines the overview of sensor technology that associates with obstacle detection system and car-like robot. This review summarizes the effectiveness and weakness of common type of sensors such as lidar, radar, ultrasonic sensor, infrared sensor, computer vision, sensor fusion and sensor array. This paper will also discuss on control methods for car-like robot that includes hand gestures, voice control, infrared remote control, Android based Bluetooth mobile control, and Wi-Fi based mobile control, outlining the effectiveness and limitation of each control method.