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1,184 results for “conversion”
Figure 10. Conceptual diagram illustrating vector quantization codeword formation-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>After the conversion process converts the analogue speech signal into a series words,<br> then converting recognized word to facial animation based on VRML.</p>
Figure 9. Mel Cepstral Coefficients in time domain-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>In this section, Feature matching Algorithm has been discussed. The goal of feature<br> matching is to classify objects into one of a number of categories or classes. In this project, Vector<br> Quantization approach will be used and the best matching result will be the desired voice.</p>
Figure 6. Change the frequency at Hertz scale to Mel Scale.-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Following computing the spectrum power and applying the above equation on frequency<br> axis, some mediating filters equal to identical overlapping are applied on the scaled spectrum and<br> each filters energy is computed as particularity. This is because conception of a particular frequency<br> by the auditory system is affected by a critical band of frequencies surrounding it. Number of filters<br> is usually between 20 and 30. Logarithmic non linear change operations on obtained particulates for<br> adjusting amplified of particularities and their important though coordinating them with the<br> structure of the auditory system following computing energy of each filter is done as follows: in the<br> following equation F1 is filter in I th, and e (i) is logarithm of energy at ith band.</p>
Figure 4. Hamming Window applied to each frame-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Use of speech spectrum for modifying work domain on signals from time to frequency is<br> made possible using Fourier coefficients. At such applications the rapid and practical way of<br> estimating the spectrum is use of rapid Fourier changes.</p>
Figure 3 Frame blocking of the speech signal-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>The next frame will begin M samples (i.e. 156 samples) after the first frame, and it will<br> overlap the first frame by N-M samples (256 – 156 = 100 samples). Then the third frame will start<br> at 2M samples after the first frame and it will overlap first frame by N-2M. The fourth frame will<br> start at 3M samples after the first, and it will overlap it by N-3M. The process will continue until all<br> input signal is accounted for. The result of this step plotted using MATLAB plot command and<br> displayed in Figure 3.<br> Figure 3 Frame blocking of the speech signal<br> The next step in the processing is to window each individual frame so as to minimize the<br> signal discontinuities at the beginning and end of each frame. The concept here is to minimize the<br> spectral distortion by using the window to taper the signal to zero at the beginning and end of each<br> frame. If we define the window as w(n), 0 ≤ n ≤ N −1, where N is the number of samples in each<br> frame, then the result of windowing is the signal<br> y (n) = x (n)w(n), 0 ≤ n ≤ N −1 l l<br> </p>
Figure 1. The Structure of the Automatic Translate Voice to Sign Language Animation System-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>All technologies of voice recognition, speaker identification and verification, each has its<br> own advantages and disadvantages and may requires different treatments and techniques. The<br> choice of which technology to use is application-specific. At the highest level, all voice recognition<br> systems contain two main modules: feature extraction and feature matching. Feature extraction is<br> the process that extracts a small amount of data from the voice signal that can later be used to<br> represent each word. Feature matching involves the actual procedure to identify the unknown word<br> by comparing extracted features from his/her voice input with the ones from a set of known words.<br> A wide range of possibilities exist for parametrically representing the speech signal for the<br> voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency<br> Cepstrum Coefficients (MFCC).</p>
Figure 8. Speech signal after frequency wrapping-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Use of speech spectrum for changing the work domain on the signal from time to frequency<br> is used via Fourier conventions. The rapid and practical way of estimating Spectrum in such<br> applications is employment of rapid Fourier transform. The final stage is extracting particularities is<br> use of discrete cosine to return particularities to the time domain and converse FFT approximation.<br> Major advantage of this method is decrease of number of particularities of number of filter from f N<br> to c N in which c f N ≤ N . In addition, doing so, includes making independent of the obtained<br> particularity and rendering them non dependant which leads matrix covariance features to become<br> axial. The following equation shows this point.</p>
Figure 7. Speech signal after FFT-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Use of speech spectrum for changing the work domain on the signal from time to frequency<br> is used via Fourier conventions. The rapid and practical way of estimating Spectrum in such<br> applications is employment of rapid Fourier transform. The final stage is extracting particularities is<br> use of discrete cosine to return particularities to the time domain and converse FFT approximation.<br> Major advantage of this method is decrease of number of particularities of number of filter from f N<br> to c N in which c f N ≤ N . In addition, doing so, includes making independent of the obtained<br> particularity and rendering them non dependant which leads matrix covariance features to become<br> axial. The following equation shows this point.</p>
Figure 2. MFCC Block Diagram Step 1-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Mel Frequency Cepstral Coefficients (MFCC) are coefficients that represent audio, based on<br> perception. It is derived from the Fourier Transform (FFT) or the Discrete Cosine Transform (DCT)<br> of the audio clip. The basic difference between the FFT/DCT and the MFCC is that in the MFCC,<br> the frequency bands are positioned logarithmically (on the Mel scale) which approximates the<br> human auditory system's response more closely than the linearly spaced frequency bands of FFT or<br> DCT. This allows for better processing of data. The main purpose of the MFCC processor is to<br> mimic the behavior of the human ears. Overall the MFCC process has 5 steps that show in figure 2.</p>
Figure 11. A sample of speech to facial animation system-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>To increase the autonomy of deaf and hard of hearing people in their day-to-day<br> professional and social lives, in this paper design and initial implementation of a new approach<br> based on MFCC and Vector Quantization Method is described. This approach includes<br> analyses of speech to animate the talking head. Our future work will include the conception of<br> new test types and performance patterns. We are particularly interested in extending this<br> approach to testing to include implementation of applications under real-time constraints</p>
Figure 5. Mel-spaced filter bank-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Physiologic changes show that human comprehension of frequency content of sound does<br> not obey a linear space. Therefore for each individual it is computed and measure with a real<br> frequency of sound peak at Mel seal. Using the below equation one can change the frequency at<br> Hertz scale to Mel Scale.</p>
The Role of Lexical Alignment in Human Understanding of Explanations by Conversational Agents
<p>This dataset was collected as part of the research investigating the role of lexical alignment in understanding. The dataset consists of the responses of 179 participants recruited via Prolific (a crowdsourcing platform).</p> <p>1. Dialogue with a conversational agent (CA)</p> <p>2. Survey questionnaire responses from the participants.</p> <p>3. Primes selected or provided by the participants.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Cleaned data sources listed above. The data is cleaned by removing data from participants who failed attention checks, or any other factor leading to error such as experiment setup, etc.</li> <li>A Python (Jupyter) playbook to analyse the collected data (Analysis_20230927.ipynb)</li> <li>Experiment design (exp_design_flow.png)</li> </ol>
Documentation artifacts for conversational SRS in chatbots: a systematic review and a new meta-model proposal and validation
<p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation, since there are not so many directions on how to register and test subjective requirements. </p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 studies that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we proposed the conversational integrated map and validated it by conducting a 2-step questionnaire among software practitioners experience in requirements engineering and chatbot requirement's specification.</p> <p>Conclusion: Future studies should investigate if existing artifacts are enough to address all complex aspects of chatbots' specific conversational requirements or require further adaptation. Future studies should investigate specific SRS needs for different types of softwares.</p>
Supplementary Material for Documentation artifacts for conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal
<p>This is a supplementary data of the tertiary systematic literature review conducted in the paper "Conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal".</p> <p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation since there are not so many directions on how to register and test subjective requirements.</p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 relevant papers that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we propose the conversational integrated map, a meta-model solution as documentation of conversational requirements.</p>
Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>Where tt time expended by the servomotors to point the laser to a given position and execute<br> a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the<br> tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point<br> the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and<br> 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested<br> between 20 and 60 minutes and N is the number of repetitions in a therapy.</p>
Partially automatically annotated corpus to predict gestural cues in Embodied Conversational Agents
<p>#Structure of the corpus</p> <p>This corpus has been built using speeches of Spanish politicians freely available <a href="http://www.congreso.es/portal/page/portal/Congreso/Congreso/Intervenciones">here</a> along with their transcriptions.</p> <p>Each transcription has been analyzed in terms of:</p> <ul> <li>Surface Syntactic Structure*</li> <li>Deep Syntactic Structure*</li> <li>Morphology (Part of Speech)*</li> <li>Communicative Structure</li> </ul> <p>Gestures (beat vs. no gesture tags) have been annotated using the videos.</p> <p>*All those features have been automatically retrieved using the parser freely available in https://github.com/TalnUPF/miis. The other features have been annotated manually.</p> <p>#Concerns about the corpus</p> <p>This corpus has been mostly annotated manually. Annotation agreement has not been computed.</p> <p>Moreover, it is small. In order to extract reliable correlations from it, it should be extended.</p>
SparBOFWEC Spar Buoy for Offshore Floating Wind Energy Conversion - Data Storage Report
<p>The present work describes the experiences gained from the design methodology and operation of a 3D physical model experiment aimed to investigate the dynamic behaviour of a spar buoy (SB) off-shore floating wind turbine (WT) under different wind and wave conditions. The physical model tests have been performed at Danish Hydraulic Institute (DHI) off-shore wave basin within the European Union-Hydralab+ Initiative, in April 2019. The floating WT model has been subjected to a combination of regular and irregular wave attacks and wind loads.</p>
Webis-Voice-based-and-Conversational-Argument-Search-20
<p>Interface, questionnaires, and collected data for the paper "<a href="https://webis.de/publications.html#?q=stein2020b">Investigating Expectations for Voice-based and Conversational Argument Search on the Web</a>".</p> <p>Data is mostly in tab-separated values format (like csv, just with tabs). For the transcripts of the user study, only the sequence of action labels (in xml) is released for privacy reasons. The labels are described in user-study-tags-participant.txt and user-study-tags-system.txt.</p>
Figure 1 in Effects of forest conversion on tce assemblages' structure of aquatic insects in subtropical regions
Figure 1. Location of tce micro-basin and sampled streams in forested area (F1, F2, and F3) and converted area (C1, C2, and C3) at Parque Estadual do Turvo and adjacent areas, in soutcern Brazil.
Figure 3 in Effects of forest conversion on tce assemblages' structure of aquatic insects in subtropical regions
Figure 3. Ordination diagram of NMDS of Epcemeroptera, Plecoptera, and Triccoptera assemblages at streams in forested area (F) and converted area (C). Numbers 1-3 refer to tce stream; R refers to rocky bottom substrate, and L refers to leaf litter substrate.
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