Voice Recognition System Research Articles

Is tonal language a problem for speaker identification (SPID)?

The availability of numerous technologies has led to an increase in the usage of bioinformatics in recent years. Siri, Alexa, and other artificial intelligence systems assist us in our daily lives. Voice recognition systems are used to confirm an individual's identity based on particular elements retrieved from his or her voice. In this regard, the current study attempted to assess the proportion of speaker identification in tonal language speaking persons. The study included 20 participants in the age range from 20 to 40 years. All participants were given a few phrases to speak and were recorded. PRAAT software was used to analyze the obtained data. A vector was developed by using the first two formants, which was then utilized to calculate the percentage for speaker identification. From small sample size to bigger sample size, three groups were formed: A-5, B-10, and C-20 speakers. In a lower sample size, results showed a benchmark of 65% for vowel /i:/, which is better for SPID, 60% for /a:/, which is above chance level, and 45% for /u:/, which is below chance level. The authors stated that increasing the sample size has an influence on speaker identification.

The role of artificial intelligence in clinical imaging and workflows

Evidence-based medicine, outcomes management, and multidisciplinary systems are laying the foundation for radiology on the cusp of a new day. Environmental and operational forces coupled with technological advancements are redefining the veterinary radiologist of tomorrow. In the past several years, veterinary image volumes have exploded, and the scale of hardware and software required to support it seems boundless. The most dynamic trend within veterinary radiology is implementing digital information systems such as PACS, RIS, PIMS, and Voice Recognition systems. While the digitization of radiography imaging has significantly improved the workflow of the veterinary radiology assistant and radiologist, tedious, redundant tasks are abundant and mind-numbing. They can lead to errors with a significant impact on patient care. Today, these boring and repetitious tasks continue to bog down patient throughput and workflow. Artificial intelligence, particularly machine learning, shows much promise to rocket the workflow and veterinary clinical imaging into a new day where the AI management of mundane tasks allows for efficiency so the radiologist can better concentrate on the quality of patient care. In this article, we briefly discuss the major subsets of artificial intelligence (AI) workflow for the radiologist and veterinary radiology assistant including image acquisition, segmentation and mensuration, rotation and hanging protocol, detection and prioritization, monitoring and registration of lesions, implementation of these subsets, and the ethics of utilizing AI in veterinary medicine.

Ink-lithographic fabrication of silver-nanocrystal-based multiaxial strain gauge sensors through the coffee-ring effect for voice recognition applications

Human voice recognition techniques have remarkable potential for clinical applications because information from acoustic signals can reflect human body conditions. This paper reports the fabrication of Ag nanocrystal (NC)-based multiaxial wearable strain gauge sensors by ink-lithography for voice recognition systems. Benefiting from the one-step-device-fabrication strategy of ink-lithography, which can yield Ag NC patterns with specific dimensions and endow physical properties, the Ag NC-based multiaxial strain sensors can be fabricated on an ultrathin substrate (~ 6 μm). Additionally, the coffee-ring effect can be induced onto the Ag NC patterns to realize high sensitivity and angle dependence (gauge factors {G}_{0^circ } = 11.7 ± 1.2 and {G}_{90^circ } = 105.5 ± 20.1); moreover, the voice onset time for voice recognition can be detected by the sensors. These features assist in distinguishing between voiced and voiceless plosive contrasts via measurements of contact-based voice onset time differences and can act as a cornerstone for further advancements in wearable sensors as well as voice recognition and analysis.Graphical

No-code robotic programming for agile production: A new markerless-approach for multimodal natural interaction in a human-robot collaboration context.

Industrial robots and cobots are widely deployed in most industrial sectors. However, robotic programming still needs a lot of time and effort in small batch sizes, and it demands specific expertise and special training, especially when various robotic platforms are required. Actual low-code or no-code robotic programming solutions are exorbitant and meager. This work proposes a novel approach for no-code robotic programming for end-users with adequate or no expertise in industrial robotic. The proposed method ensures intuitive and fast robotic programming by utilizing a finite state machine with three layers of natural interactions based on hand gesture, finger gesture, and voice recognition. The implemented system combines intelligent computer vision and voice control capabilities. Using a vision system, the human could transfer spatial information of a 3D point, lines, and trajectories using hand and finger gestures. The voice recognition system will assist the user in parametrizing robot parameters and interacting with the robot’s state machine. Furthermore, the proposed method will be validated and compared with state-of-the-art “Hand-Guiding” cobot devices within real-world experiments. The results obtained are auspicious, and indicate the capability of this novel approach for real-world deployment in an industrial context.

Biometric Voice Recognition system using MFCC and GMM with EM

Biometric Voice Recognition system using MFCC and GMM with EM

Android Controlled Smart Notice Board Using IoT

Abstract: Notice boards are playing very important role in our day to day life. By replacing conventional analog type notice board with digital notice board we can make information dissemination much easier in a paperless community. Here the authorized user can control notice board through internet. So information can be sent anywhere in the world and can be displayed within seconds. Information may be in the form of Text, Image, PDF, video etc. Android application is used for sending information and Raspberry Pi is connected to the internet at the receiving side. In addition to this a voice recognition system is installed to access the information over voice. The smart notice board also sends the information to the provided Gmail. A URL provided for the smart notice board that will be accessible through any browser and displayed content can be downloaded.

PERANCANGAN SISTEM KEAMANAN LINGKUNGAN PENGENAL SUARA KULKUL DENGAN MENGGUNAKAN METODE DEEP LEARNING

Advances in information technology provide benefits for people's lives today. Before the existence of information technology, people still use traditional communication media called kulkul. Kulkul is a communication tool that is used by hitting. Over time, people began to leave this culture because of the many other information systems used as communication media. However, nowadays people in their teens still do not know what the sound of the kulkul means. This is due to the absence of a kulkul voice recognition website. The method of data collection in this study was the observation method of a kulkul worker located in Denjalan Subvillage, Batubulan, Sukawati, Gianyar. To build a kulkul voice recognition system, the author uses a deep learning method. In this system there are 2 processes, namely training and classification. The training is used for the system to learn to recognize the sound of the kulkul and the classification to determine the category of the kulkul sound. Based on the classification carried out, the results obtained is testing accuracy 85%.

Wheelchair Assistance and Guidance Using IOT

A Smart Wheelchair is a Power Wheelchair with numerous sensors, assistive technology, and computers built in that allow a disabled person to travel around freely and safely. People with disabilities, such as impairments, handicaps, and life-altering injuries, are the users. The conventional wheelchairs are being increasingly replaced by these new models. Only 5 to 15% of the 70 million disabled individuals worldwide, according to the World Health Organization (WHO), have access to wheelchairs. As a result, we must supply a cost-effective Smart that uses cutting-edge components and technology while keeping costs to a minimum. They have embraced a number of technologies, including artificial intelligence, where they created an autonomous wheelchair that used machine learning principles to travel. Some of them have also used the Internet of Things to manage the wheelchair using a voice recognition system. This report will outline an inexpensive Smart Wheelchair-based Arduino Nano microcontroller and Internet of Things (IoT) technology that has a number of features to help disabled people, particularly poor people who cannot afford an expensive Smart Wheelchair, get the assistance they need to complete daily tasks without help. As a result of this project, a variety of disabled people will be able to afford smart wheelchairs. It will be powered by Arduino Nano, ESP-12e module for Wi-Fi access, MPU6050 for fall detection with voice message notification using IFTTT platform, obstacle detection with buzzer and LED for hazard detection, voice recognition system, and joysticks for wheelchair control.

Design of an integrated learning approach to assist real-time deaf application using voice recognition system

Modern voice recognition is the process of predicting the human voice using emergent artificial intelligence techniques. It is extensively adopted in real-time applications for identity verification, helping deaf/dumb people, electronic voice eavesdropping, and hearing-impairment purposes. Predicting salient and discriminative process flow in the voice recognition process is the most challenging task in identifying the voice. Some existing research works find it complex to provide better prediction results. However, this work concentrates on modelling an efficient approach to predict the voice using deep learning approaches and it includes four essential phases: data acquisition phase, input voice pre-processing phase, word segmentation phase and classification. For experimentation purpose, an available online dataset was initially acquired and fed as an input to the proposed model. Subsequently, the provided input is pre-processed using an improved Mel-DCT filter for voice activity detection and to improve the classifier performance. The pre-processed image is then supplied into the word segmentation phase where the words from the input data are segmented using the Grab Cut segmentation. Finally, the classification was done with the integrated Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). The integration is performed to handle the drawbacks of the single classifier model and to boost the prediction accuracy.

Use of Neural Networks in Voice Commands Recognition

The purpose of the article is to research, analyze and consider general problems and prospects for the development of voice command recognition systems using the capabilities of neural networks, using the latest neural network technologies. The research methodology consists in methods of semantic analysis of this subject area’s basic concepts (voice command recognition systems). The existing systems and recognition algorithms are considered in the article. The scientific novelty of the research is the analysis of modern voice recognition systems, the results of which can be used in the development of their own recognition system based on the use of improved speech models and recurrent neural network learners. Conclusions. The efficiency of using neural networks for voice command recognition tasks is proved. Based on the research, a speech recognition system based on neural networks has been developed using an improved speech model.

A novel clinical trials imaging network platform for RECIST 1.1, and related protocols run on standard radiology software: A pilot at multiple oncology centers.

e13552 Background: Oncologists face the following challenges with clinical trials imaging analysis: non-structured radiology reports, inconsistent lesion measurement between time points, time-consuming and error-prone manual calculations for clinical trials protocols, and lack of dedicated storage and auditable signoff. Clinical Trials Imaging Network (CTIN) is a novel clinical trials imaging platform that allows radiologists to provide oncologists with dedicated clinical trials imaging reads and calculations from non-proprietary standard radiology dictation and PACS software, using RECIST 1.1 and other tumor response criteria. It was piloted at several cancer centers between 2019 and 2021. Methods: An anonymous survey was sent out to the oncologist and research coordinators who have experience using the CTIN program. The survey was designed to gauge the effectiveness of the software. Questions asked included the decrease in turnaround time between initiation of scan and completion of research protocol calculations, and regarding the decrease in time the oncologists personally need to make calculations. Additionally, respondents were inquired regarding the accuracy of the data, and how much time they had to spend verifying the integrity of the data. Results: The survey was sent to approximately 150 recipients, with 32 respondents. 84.4% of respondents said they strongly agreed or agreed that there was a decreased turnaround time between scan initiation, and completion of research protocol calculations, and 15.6% neither agreed nor disagreed. 80% strongly agreed or agreed that there was a decreased time they personally performed the calculations. 25.0% said they saved less than 5 minutes per time point personally performing the calculations, 28.6% said 5-15 minutes, 23.4% 15 to 30 minutes, and 17.8% 30-60 minutes. Users were also asked regarding their time spend verifying the data. 64.5% of people spent less than 5 minutes per time point verifying the integrity and quality of the data. 19.4% spent 5-15 minutes, 12.9% spent 15-30 minutes, and 3.2% 30-60 minutes. No one spent greater than 60 minutes verifying the data. 65.6% of people strongly agreed that they were confident in the accuracy of the data, 25.0% agreed, and 9.4% neither agreed or disagreed. 90.6% of people said that they were very satisfied or satisfied with CTIN. Conclusions: We have piloted an approach to provide clinical trials imaging metrics to oncologists using non-proprietary technology that runs from any standard radiology voice recognition system and PACS. The majority of respondents agreed that they would recommend this system for its time-saving, automated calculation and ease of use, user features. The CTIN platform bridges the gap between general radiology reads and clinical trials imaging reads without the need for dedicated radiology software.

Voice to Sign Language Converter

Abstract: The aim of this paper is to design a convenient system that is helpful for the people who have hearing difficulties and in general who use very simple and effective method; sign language. This system can be used for converting sign language to voice and also voice to sign language. A motion capture system is used for sign language conversion and a voice recognition system for voice conversion. It captures the signs and dictates on the screen as writing. It also captures the voice and displays the sign language meaning on the screen as motioned image or video Keywords: Motioned Image, Sign Language Converter, Voice Recognition.

A Feasibility Study on Hand Gesture Intention Interpretation Based on Gesture Detection and Speech Recognition

In this paper, considering the characteristics of hand gestures that occur during a natural, unscripted human conversation, we report the results of experiments involving the data analysis of the video recordings of experiments. The recorded conversations between two subjects and the assistant in the experiments (the first author) are analyzed for findings that might be useful to future human-robot interaction (HRI), findings regarding the recognition of hand gestures in natural conversations. In the experiment, hand gestures that appeared naturally during the conversations were manually selected, and their elapsed times and number of appearances were analyzed. In addition, these unscripted conversations were analyzed with a voice recognition system, and the meanings of the hand gestures that appear in the recorded video were interpreted. It was observed that some gestures were made consciously and others unconsciously. Some gestures appeared multiple times with similar meanings and the same intention, especially gestures involving pointing at something to emphasize a sentence or word. Other observations included that there were times when (i) each subject made different hand gestures to convey the same meaning, (ii) both subjects made the same hand gesture but intended different meanings, and (iii) a specific gesture was made by the same subject but with different intended meanings. After considering the results of these experiments, we conclude that it is better to combine gestures with the utterances at the time in order to interpret their intended meanings correctly.

Voice Liveness Detection for Voice Assistants Through Ear Canal Pressure Monitoring

The voice assistants are important input devices in future smart homes. Thanks to the great performance that is provided by current voice recognition systems, current voice assistants can understand various commands in different language from users and connect with other devices in the same local network to perform corresponding actions. However, the voices are not secure due to its nature. Even if we secure voice assistant using the voiceprint, attackers can still steal the victim's voices and replay them to voice assistants for attacking purpose. In this paper, we propose a new voice liveness detection system that is specifically designed for voice assistants. The key insight behind our system is that users will open their mouth when they say some phonemes. Such opening mouth activities will impact the air pressure in the ear canal if the ear canal is an enclosed space. Therefore, we can detect the liveness of the voices on the side of voice assistants by cauterizing the correlations between each sentence and the air pressure. Experiments with ten volunteers show that our system can accurately accept voice commands from legitimate users with accuracy of 94.8% and 97%. Moreover, our system can effectively defend current voice assistant devices from replay attacks with accuracy of 99.25% and 99.5%.

P125 The advancement of digital health technology in the outpatient setting, is speech recognition software useful?

Abstract Background/Aims Over the last 20 years, innovation in digital health technology have been propelling health systems forwards. With the advent of the electronic health record, digital prescribing and artificial intelligence and machine learning comes new opportunities. The use of voice recognition systems in the transcription of rheumatology clinics has the potential for cost and time effectiveness which is ever important within the NHS. Here we review the use, user-interface acceptability and potential cost savings. Methods In speech recognition software the speech is converted to a sequence of words in written text. This is not a new technology and in particular has been widely used within radiology departments across the UK. Rheumatology is largely a clinic-based specialty, with high output of clinic letters. Speech recognition software removes the need for transcription-based services. As a new technology we have reviewed its use within the rheumatology, dermatology, endocrinology, renal and paediatric departments at a large district general hospital. Results 143 staff members have been provided with a license and on average 64% are regularly using the available software. Over 3 months an average of 14,843 minutes per month of dictation has been completed. It has been estimated that over 12 months’ use there has been £100,000 savings through administerial time saved. The average turnaround time for clinic letters from dictation to delivery (electronically) to GPs has improved from approximately 1-2 weeks (but delays up to 4 weeks seen) to an approximately 24-36 hours. Preliminary data from users of this software suggest that compared to traditional dictation devices: 33% found it ‘much better’, 33% ‘better’ and 33% ‘the same’. 67% of participants found it ‘easy, or very easy’ to use whilst 16% found it ‘difficult’. 50% of participants found it ‘usually’ saved time, 33% ‘sometimes’ and 16% ‘rarely’ saved time. Conclusion This preliminary data suggest potential time and cost savings with largely positive feedback from users. Further work is needed to assess potential patient safety issues including errors and inaccuracies compared to traditional dictation means. We aim to complete this work prior to presentation of this abstract should it be accepted. Disclosure H. Crawshaw: None. S. Jamal: None. R. Andev: None.

An Effective Artificial Intelligence-Enabled Error Detection and Accuracy Estimation Technique for English Speech Recognition System

Error detection and accuracy estimation in automated speech recognition (ASR) systems act a vital part in the design of human-computer spoken dialogue systems, as recognition error can hamper accurate systems in understanding the end user intentions. The major aim is to identify the errors in an utterance, and therefore, the dialogue manager can provide proper clarifications to the user. Therefore, the design of accurate error detection and accuracy determination techniques becomes essential in the ASR system. With this motivation, this paper presents a novel artificial intelligence-enabled accuracy estimation and error detection technique for the English speech recognition system (AIEDAE-ESRS). The goal of the AIEDAE-ESRS technique is to perform three actions such as confidence estimation, out-of-vocabulary (OOV) word identification, and error type categorization. In addition, the AIEDAE-ESRS technique performs different levels of preprocessing including sampling of input speech signal, bandpass filtering, and noise removal. Besides, a new deep neural network with hidden Markov model- (DNN-HMM-) based speech recognition technology is designed, which also aims to estimate the accuracy and error. Finally, the hyperparameters of the DNN-HMM model can be optimally chosen by the use of flower pollination algorithm (FPA) and thereby accomplished improved recognition performance. In order to demonstrate the better performance of the AIEDAE-ESRS technique, a series of simulations were conducted and the results are examined under varying aspects. English voice recognition system’s accuracy estimation and error detection were made possible using artificial intelligence (AIEDAE-ESRS). There are three steps in the AIEDAE-ESRS method: confidence estimation; identifying out-of-vocabulary words (OOV); and categorizing mistake types. The simulation results reported the enhanced performance of the AIEDAE-ESRS methodology over current advanced approaches. Our AIEDAE-ESRS methodology outperforms existing methodologies by a factor of ten. The simulation results demonstrated that the AIEDAE-ESRS methodology outperformed previous approaches in terms of efficiency. The improved experimental results indicated that the AIEDAE-ESRS technique produced superior results across a variety of measures.

Development of Directional MEMS Microphone Single Module for High Directivity and SNR

A unidirectional microelectromechanical system (MEMS) microphone single module for handsfree and voice recognition systems in automobiles is presented. Because in-cabin noise and temperature variation affect the reduction in the handsfree and voice recognition system performance, noise suppression and increasing the signal-to-noise ratio (SNR) of the microphone are required. In this study, a capacitive MEMS microphone module with a high SNR and a unidirectional characteristic is achieved by designing the structure, package, and module of the MEMS microphone. To improve the SNR, the microphone is developed using a slit-edged membrane. The slit structure is designed to release the residual stress of the membrane to achieve improved sensitivity and SNR. The unidirectional characteristic of the microphone enables suppression of noise signals from undesired directions. The directional characteristic of the microphone is realized by attaching a porous SU-8 filter to delay the time to one of the two acoustic ports on the package. Tests on the proposed unidirectional MEMS microphone package and module show that an SNR of 62.4 dB and a front-back ratio of 27.1 dB are achieved.

Integrating of voice recognition email application system for visually impaired person using linear regression algorithm

The outcome of this study will surely help visually impaired people, who face difficulties in accessing the computer system. Voice recognition will help them to access e-mail. This study also reduces cognitive load taken by a visually impaired users to remember and type characters using keyboard. If this system is implemented, self-esteem and social and emotional well-being of the visually impaired users will be lifted up for they will now feel they are being valued in the society and has fair treatment and access in technologyThe main function of this study is to use a keyboard of the user that will respond through voice. The purpose of this study is to help a visually impaired person to use modernize application to interact with voice recognition system with the use of email into different types of modern gadgets Line computers or mobile phones. In terms of Functionality of the application, the proponents will use a set of APIs’ or Application Program Interface such as Google Speech-to-text and text-to-speech application and it will process though Email System and also the SNMTP or Simple Network Management Protocol will be used for mailing services, in programming software, the proponent will be using PHP for the backend of web interface. HTML and CSS is the front end programming for the creation of Web Base User Interface that will be used. Voice typing and Dictation Speech Interaction models using windows dictation engine. The proponent used descriptive research design in this study. Descriptive research design is being used by the proponents to describe the characteristics of a population or phenomenon of visually impaired persons being studied. Descriptive research is mainly done because the researchers wants to gain a better understanding for a topic. It focuses on providing information that is useful in the development. The research is based on mixed method focused in producing such informative outcomes that can be used. Based on the results of the surveys, conclusions were drawn: Majority of the respondents were male adultery period ranging ages 32-41.all are working in the massage therapist. Majority of the respondents rated the overall function of the application Excellent and rated the level of security of the application is Secured.

Integrating of voice recognition email application system for visually impaired person using linear regression algorithm


 
 
 The outcome of this study will surely help visually impaired people, who face difficulties in accessing the computer system. Voice recognition will help them to access e-mail. This study also reduces cognitive load taken by a visually impaired users to remember and type characters using keyboard. If this system is implemented, self-esteem and social and emotional well-being of the visually impaired users will be lifted up for they will now feel they are being valued in the society and has fair treatment and access in technologyThe main function of this study is to use a keyboard of the user that will respond through voice. The purpose of this study is to help a visually impaired person to use modernize application to interact with voice recognition system with the use of email into different types of modern gadgets Line computers or mobile phones. In terms of Functionality of the application, the proponents will use a set of APIs’ or Application Program Interface such as Google Speech-to-text and text-to-speech application and it will process though Email System and also the SNMTP or Simple Network Management Protocol will be used for mailing services, in programming software, the proponent will be using PHP for the backend of web interface. HTML and CSS is the front end programming for the creation of Web Base User Interface that will be used. Voice typing and Dictation Speech Interaction models using windows dictation engine. The proponent used descriptive research design in this study. Descriptive research design is being used by the proponents to describe the characteristics of a population or phenomenon of visually impaired persons being studied. Descriptive research is mainly done because the researchers wants to gain a better understanding for a topic. It focuses on providing information that is useful in the development. The research is based on mixed method focused in producing such informative outcomes that can be used. Based on the results of the surveys, conclusions were drawn: Majority of the respondents were male adultery period ranging ages 32-41.all are working in the massage therapist. Majority of the respondents rated the overall function of the application Excellent and rated the level of security of the application is Secured.
 
 

A Natural Language Interface for an Autonomous Camera Control System on the da Vinci Surgical Robot

Positioning a camera during laparoscopic and robotic procedures is challenging and essential for successful operations. During surgery, if the camera view is not optimal, surgery becomes more complex and potentially error-prone. To address this need, we have developed a voice interface to an autonomous camera system that can trigger behavioral changes and be more of a partner to the surgeon. Similarly to a human operator, the camera can take cues from the surgeon to help create optimized surgical camera views. It has the advantage of nominal behavior that is helpful in most general cases and has a natural language interface that makes it dynamically customizable and on-demand. It permits the control of a camera with a higher level of abstraction. This paper shows the implementation details and usability of a voice-activated autonomous camera system. A voice activation test on a limited set of practiced key phrases was performed using both online and offline voice recognition systems. The results show an on-average greater than 94% recognition accuracy for the online system and 86% accuracy for the offline system. However, the response time of the online system was greater than 1.5 s, whereas the local system was 0.6 s. This work is a step towards cooperative surgical robots that will effectively partner with human operators to enable more robust surgeries. A video link of the system in operation is provided in this paper.