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Journal articles on the topic 'Voice recognition'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

Sangani, K. "Inner voice [voice recognition]." Engineering & Technology 8, no. 7 (August 1, 2013): 36–37. http://dx.doi.org/10.1049/et.2013.0717.

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2

Mehta, Amit, and Theresa C. McLoud. "Voice Recognition." Journal of Thoracic Imaging 18, no. 3 (July 2003): 178–82. http://dx.doi.org/10.1097/00005382-200307000-00007.

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3

Schreiber, Melvyn H. "Voice recognition." Academic Radiology 5, no. 12 (December 1998): 871–72. http://dx.doi.org/10.1016/s1076-6332(98)80251-1.

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4

Schweinberger, Stefan R., Anja Herholz, and Volker Stief. "Auditory Long term Memory: Repetition Priming of Voice Recognition." Quarterly Journal of Experimental Psychology Section A 50, no. 3 (August 1997): 498–517. http://dx.doi.org/10.1080/713755724.

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Two experiments examined repetition priming in the recognition of famous voices. In Experiment 1, reaction times for fame decisions to famous voice samples were shorter than in an unprimed condition, when voices were primed by a different voice sample of the same person having been presented in an earlier phase of the experiment. No effect of voice repetition was observed for non-famous voices. In Experiment 2, it was investigated whether this priming effect is voice-specific or whether it is related to post-perceptual processes in person recognition. Recognizing a famous voice was again primed by having earlier heard a different voice sample of that person. Although an earlier exposure to that person's name did not cause any priming, there was some indication of priming following an earlier exposure to that person's face. Finally, earlier exposure to the identical voice sample (as compared to a different voice sample from the same person) caused a considerable bias towards responding “famous”—i.e. performance benefits for famous but costs for nonfamous voices. The findings suggest that (1) repetition priming invoice recognition primarily involves the activation of perceptual representations of voices, and (2) it is important to determine the conditions in which priming causes bias effects that need to be disentangled from performance benefits.
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5

Doyle, Sean. "Determining voice recognition accuracy in a voice recognition system." Journal of the Acoustical Society of America 128, no. 2 (2010): 964. http://dx.doi.org/10.1121/1.3481753.

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6

Plante-Hébert, Julien, Victor J. Boucher, and Boutheina Jemel. "The processing of intimately familiar and unfamiliar voices: Specific neural responses of speaker recognition and identification." PLOS ONE 16, no. 4 (April 16, 2021): e0250214. http://dx.doi.org/10.1371/journal.pone.0250214.

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Research has repeatedly shown that familiar and unfamiliar voices elicit different neural responses. But it has also been suggested that different neural correlates associate with the feeling of having heard a voice and knowing who the voice represents. The terminology used to designate these varying responses remains vague, creating a degree of confusion in the literature. Additionally, terms serving to designate tasks of voice discrimination, voice recognition, and speaker identification are often inconsistent creating further ambiguities. The present study used event-related potentials (ERPs) to clarify the difference between responses to 1) unknown voices, 2) trained-to-familiar voices as speech stimuli are repeatedly presented, and 3) intimately familiar voices. In an experiment, 13 participants listened to repeated utterances recorded from 12 speakers. Only one of the 12 voices was intimately familiar to a participant, whereas the remaining 11 voices were unfamiliar. The frequency of presentation of these 11 unfamiliar voices varied with only one being frequently presented (the trained-to-familiar voice). ERP analyses revealed different responses for intimately familiar and unfamiliar voices in two distinct time windows (P2 between 200–250 ms and a late positive component, LPC, between 450–850 ms post-onset) with late responses occurring only for intimately familiar voices. The LPC present sustained shifts, and short-time ERP components appear to reflect an early recognition stage. The trained voice equally elicited distinct responses, compared to rarely heard voices, but these occurred in a third time window (N250 between 300–350 ms post-onset). Overall, the timing of responses suggests that the processing of intimately familiar voices operates in two distinct steps of voice recognition, marked by a P2 on right centro-frontal sites, and speaker identification marked by an LPC component. The recognition of frequently heard voices entails an independent recognition process marked by a differential N250. Based on the present results and previous observations, it is proposed that there is a need to distinguish between processes of voice “recognition” and “identification”. The present study also specifies test conditions serving to reveal this distinction in neural responses, one of which bears on the length of speech stimuli given the late responses associated with voice identification.
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7

Haag, Andreas. "VOICE CONTROL WITH SEMANTIC VOICE RECOGNITION." ATZelektronik worldwide 7, no. 6 (October 2012): 50–53. http://dx.doi.org/10.1365/s38314-012-0136-8.

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8

Searcy, Gus, and Franz Kavan. "Voice recognition system." Journal of the Acoustical Society of America 93, no. 6 (June 1993): 3541–42. http://dx.doi.org/10.1121/1.405347.

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9

Searcy, Gus. "Voice recognition system." Journal of the Acoustical Society of America 94, no. 2 (August 1993): 1181. http://dx.doi.org/10.1121/1.406911.

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10

Muroi, Tetsuya. "Voice recognition system." Journal of the Acoustical Society of America 90, no. 3 (September 1991): 1711. http://dx.doi.org/10.1121/1.401729.

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11

Kimura, Shinta, and Toru Sanada. "Voice recognition system." Journal of the Acoustical Society of America 91, no. 5 (May 1992): 3088. http://dx.doi.org/10.1121/1.402882.

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12

Porter, Edward W. "Voice recognition system." Journal of the Acoustical Society of America 89, no. 5 (May 1991): 2483. http://dx.doi.org/10.1121/1.400849.

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13

Nitta, Tsuneo. "Voice recognition apparatus." Journal of the Acoustical Society of America 82, no. 3 (September 1987): 1105. http://dx.doi.org/10.1121/1.395831.

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14

Sakoe, Hiroaki. "Voice recognition system." Journal of the Acoustical Society of America 84, no. 4 (October 1988): 1580. http://dx.doi.org/10.1121/1.397211.

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15

Seo, Hiroshi. "Voice recognition system." Journal of the Acoustical Society of America 119, no. 3 (2006): 1309. http://dx.doi.org/10.1121/1.2185059.

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16

Powers, Ron. "Computer voice recognition." American Journal of Orthodontics and Dentofacial Orthopedics 117, no. 4 (April 2000): 504–6. http://dx.doi.org/10.1016/s0889-5406(00)70007-2.

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17

Niyada, Katsuyuki. "Voice recognition method." Journal of the Acoustical Society of America 96, no. 3 (September 1994): 1950. http://dx.doi.org/10.1121/1.410128.

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18

Yoda, Shoutarou. "Voice recognition apparatus." Journal of the Acoustical Society of America 115, no. 2 (2004): 459. http://dx.doi.org/10.1121/1.1669320.

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19

Seo, Hiroshi. "Voice recognition system." Journal of the Acoustical Society of America 120, no. 4 (2006): 1769. http://dx.doi.org/10.1121/1.2372382.

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20

Colman, Stephen. "Voice Recognition Evidence." Journal of Criminal Law 80, no. 1 (February 2016): 5–7. http://dx.doi.org/10.1177/0022018315624200.

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21

Hansen, G. C., K. H. Falkenbach, and I. Yaghmai. "Voice recognition system." Radiology 169, no. 2 (November 1988): 580. http://dx.doi.org/10.1148/radiology.169.2.3175016.

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22

Manzanero, Antonio L., and Susana Barón. "Recognition and discrimination of unfamiliar male and female voices." Behavior & Law Journal 3, no. 1 (December 1, 2017): 52–60. http://dx.doi.org/10.47442/blj.v3.i1.44.

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The aim of this study was examined the ability to identify voices of unfamiliar people. In experiment 1, participants performed tried to recognize the voice of unfamiliar man or woman. Results showed that subjects generally matched 83.11% when the target voice was present and made 56.45% false alarms when it was not. Discrimination was different from chance and subjects used liberal response criteria. In experiment 2, men and women tried to identify the same voices of men and women as in previous experiment. Between stimulus presentation and the recognition task, subjects listened instrumental music for 2.38 minutes, with the aim of making it harder that the voice remain active in working memory. Results showed that ability of men and women to identify an unfamiliar voice was null, in both cases with liberal response criterion. Men matched 12.06%, with 65.51% false alarms, and women 25.80% and 56.45% respectively. There was no differences in the ability to identify male and female voices, although women tend to choose more than men, even when no target voice was present.
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23

Kriegstein, Katharina von, Andreas Kleinschmidt, Philipp Sterzer, and Anne-Lise Giraud. "Interaction of Face and Voice Areas during Speaker Recognition." Journal of Cognitive Neuroscience 17, no. 3 (March 2005): 367–76. http://dx.doi.org/10.1162/0898929053279577.

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Face and voice processing contribute to person recognition, but it remains unclear how the segregated specialized cortical modules interact. Using functional neuroimaging, we observed cross-modal responses to voices of familiar persons in the fusiform face area, as localized separately using visual stimuli. Voices of familiar persons only activated the face area during a task that emphasized speaker recognition over recognition of verbal content. Analyses of functional connectivity between cortical territories show that the fusiform face region is coupled with the superior temporal sulcus voice region during familiar speaker recognition, but not with any of the other cortical regions normally active in person recognition or in other tasks involving voices. These findings are relevant for models of the cognitive processes and neural circuitry involved in speaker recognition. They reveal that in the context of speaker recognition, the assessment of person familiarity does not necessarily engage supra-modal cortical substrates but can result from the direct sharing of information between auditory voice and visual face regions.
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24

Holmes, Emma, Grace To, and Ingrid S. Johnsrude. "How Long Does It Take for a Voice to Become Familiar? Speech Intelligibility and Voice Recognition Are Differentially Sensitive to Voice Training." Psychological Science 32, no. 6 (May 12, 2021): 903–15. http://dx.doi.org/10.1177/0956797621991137.

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When people listen to speech in noisy places, they can understand more words spoken by someone familiar, such as a friend or partner, than someone unfamiliar. Yet we know little about how voice familiarity develops over time. We exposed participants ( N = 50) to three voices for different lengths of time (speaking 88, 166, or 478 sentences during familiarization and training). These previously heard voices were recognizable and more intelligible when presented with a competing talker than novel voices—even the voice previously heard for the shortest duration. However, recognition and intelligibility improved at different rates with longer exposures. Whereas recognition was similar for all previously heard voices, intelligibility was best for the voice that had been heard most extensively. The speech-intelligibility benefit for the most extensively heard voice (10%–15%) is as large as that reported for voices that are naturally very familiar (friends and spouses)—demonstrating that the intelligibility of a voice can be improved substantially after only an hour of training.
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25

Du, Yuxia. "Teacher Voice Feature Extraction and Recognition Based on Health Belief Model." Wireless Communications and Mobile Computing 2022 (March 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/4662547.

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Teachers are professional voice workers who complete their education and teaching tasks primarily through spoken English. Teachers’ voices are the most important tool for completing their professional tasks, as well as an important part of their public image. This paper proposes innovative design strategies with health management as the core concept, in light of the voice health problems of primary and secondary school teachers in the era of intelligence. For a long time, China has had a high percentage of teachers with voice problems. Teachers’ oral English expression is affected by voice diseases, which reduces teaching efficiency. Teachers’ physical and mental health are also jeopardized, and their professional identities are diminished. It is primarily caused by a lack of health-care awareness, excessive use of voice in daily life, incorrect vocalization techniques, a lack of knowledge and methods for voice protection, and ignorance on the part of education management departments. As a result, we must reduce the frequency with which we use our voices and employ scientific feature recognition methods to protect our voices.
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26

Zumalt, Joseph R. "Voice Recognition Technology: Has It Come of Age?" Information Technology and Libraries 24, no. 4 (December 1, 2005): 180. http://dx.doi.org/10.6017/ital.v24i4.3382.

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<span>Voice recognition software allows computer users to bypass their keyboards and use their voices to enter text. While the library literature is somewhat silent about voice recognition technology, the medical and legal communities have reported some success using it. Voice recognition software was tested for dictation accuracy and usability within an agriculture library at the University of Illinois. Dragon NaturallySpeaking 8.0 was found to be more accurate than speech recognition within Microsoft Office 2003. Helpful Web sites and a short history regarding this breakthrough technology are included.</span>
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27

Abe, Yoshiharu. "Method of boundary estimation for voice recognition and voice recognition device." Journal of the Acoustical Society of America 104, no. 2 (August 1998): 618. http://dx.doi.org/10.1121/1.423398.

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28

Fujimoto, Junichiroh, and Tetsuya Muroi. "Voice recognition system using voice power patterns." Journal of the Acoustical Society of America 89, no. 3 (March 1991): 1488. http://dx.doi.org/10.1121/1.400617.

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29

Karande, Pramod, Shubham Borchate, Bhavesh Chaudhary, and Prof Deveshree Wankhede. "Virtual Desktop Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1916–20. http://dx.doi.org/10.22214/ijraset.2022.41024.

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Abstract: Voice Assistants are becoming immensely popular feature that has changed the way user interact with devices. Voice assistants are used in many devices like mobile phones, laptops. These voice assistants are based on Artificial-Intelligence and Natural Language Processing. They take human voices as input and give output in integrated voices. This voice assistant takes voice through microphone, we have used libraries like pyttsx3 to convert text-to-speech. Keywords: Voice Assistant, python, pyttsx3, Artificial Intelligence, Speech Recognition
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30

Holmes, Emma, Ysabel Domingo, and Ingrid S. Johnsrude. "Familiar Voices Are More Intelligible, Even if They Are Not Recognized as Familiar." Psychological Science 29, no. 10 (August 10, 2018): 1575–83. http://dx.doi.org/10.1177/0956797618779083.

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We can recognize familiar people by their voices, and familiar talkers are more intelligible than unfamiliar talkers when competing talkers are present. However, whether the acoustic voice characteristics that permit recognition and those that benefit intelligibility are the same or different is unknown. Here, we recruited pairs of participants who had known each other for 6 months or longer and manipulated the acoustic correlates of two voice characteristics (vocal tract length and glottal pulse rate). These had different effects on explicit recognition of and the speech-intelligibility benefit realized from familiar voices. Furthermore, even when explicit recognition of familiar voices was eliminated, they were still more intelligible than unfamiliar voices—demonstrating that familiar voices do not need to be explicitly recognized to benefit intelligibility. Processing familiar-voice information appears therefore to depend on multiple, at least partially independent, systems that are recruited depending on the perceptual goal of the listener.
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31

Hamza, Motaz, Touraj Khodadadi, and Sellappan Palaniappan. "A Novel automatic voice recognition system based on text-independent in a noisy environment." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3643. http://dx.doi.org/10.11591/ijece.v10i4.pp3643-3650.

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Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.
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32

Van Lancker, Diana, Jody Kreiman, and Karen Emmorey. "Familiar voice recognition: patterns and parameters Part I: Recognition of backward voices." Journal of Phonetics 13, no. 1 (January 1985): 19–38. http://dx.doi.org/10.1016/s0095-4470(19)30723-5.

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33

Singh, Sarbjeet, Sukhvinder Singh, Mandeep Kour, and Sonia Manhas. "Voice Recognition In Automobiles." International Journal of Computer Applications 6, no. 6 (September 10, 2010): 7–11. http://dx.doi.org/10.5120/1084-1414.

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34

Bergeron, Bryan P. "Usable voice-recognition technology." Postgraduate Medicine 102, no. 5 (November 1997): 39–44. http://dx.doi.org/10.3810/pgm.1997.11.348.

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35

Stogel, Scott S. "Voice recognition dialing system." Journal of the Acoustical Society of America 100, no. 4 (1996): 1944. http://dx.doi.org/10.1121/1.417900.

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36

Sherman, William F. "Voice recognition peripheral device." Journal of the Acoustical Society of America 119, no. 3 (2006): 1308. http://dx.doi.org/10.1121/1.2185053.

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37

Thomas, Randall L. "Voice recognition technology reconsidered." Case Manager 10, no. 1 (January 1999): 22–23. http://dx.doi.org/10.1016/s1061-9259(99)80194-5.

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38

Mclaughlin, L. "Silencing voice recognition technology." IEEE Intelligent Systems 19, no. 3 (May 2004): 4–6. http://dx.doi.org/10.1109/mis.2004.12.

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39

Chang, Chienchung. "Distributed voice recognition system." Journal of the Acoustical Society of America 113, no. 1 (2003): 27. http://dx.doi.org/10.1121/1.1554228.

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40

Bi, Ning. "Voice recognition rejection scheme." Journal of the Acoustical Society of America 114, no. 5 (2003): 2543. http://dx.doi.org/10.1121/1.1634107.

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41

Shimada, Keiko. "Voice recognition dialing unit." Journal of the Acoustical Society of America 96, no. 3 (September 1994): 1951. http://dx.doi.org/10.1121/1.410129.

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42

Jacobs, Paul E., and Chienchung Chang. "Distributed voice recognition system." Journal of the Acoustical Society of America 115, no. 2 (2004): 458. http://dx.doi.org/10.1121/1.1669315.

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43

Der Ghazarian, Viken, and Ohanes Der Ghazarian. "Breathalyzer with voice recognition." Journal of the Acoustical Society of America 116, no. 6 (2004): 3256. http://dx.doi.org/10.1121/1.1852983.

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44

Garudadri, Harinath. "Method for voice recognition." Journal of the Acoustical Society of America 121, no. 5 (2007): 2494. http://dx.doi.org/10.1121/1.2739201.

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45

Hailstone, Julia C., Sebastian J. Crutch, and Jason D. Warren. "Voice Recognition in Dementia." Behavioural Neurology 23, no. 4 (2010): 163–64. http://dx.doi.org/10.1155/2010/352490.

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46

Wang, Xiaochen, and Tao Wang. "Voice Recognition and Evaluation of Vocal Music Based on Neural Network." Computational Intelligence and Neuroscience 2022 (May 20, 2022): 1–9. http://dx.doi.org/10.1155/2022/3466987.

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Artistic voice is the artistic life of professional voice users. In the process of selecting and cultivating artistic performing talents, the evaluation of voice even occupies a very important position. Therefore, an appropriate evaluation of the artistic voice is crucial. With the development of art education, how to scientifically evaluate artistic voice training methods and fairly select artistic voice talents is an urgent need for objective evaluation of artistic voice. The current evaluation methods for artistic voices are time-consuming, laborious, and highly subjective. In the objective evaluation of artistic voice, the selection of evaluation acoustic parameters is very important. Attempt to extract the average energy, average frequency error, and average range error of singing voice by using speech analysis technology as the objective evaluation acoustic parameters, use neural network method to objectively evaluate the singing quality of artistic voice, and compare with the subjective evaluation of senior professional teachers. In this paper, voice analysis technology is used to extract the first formant, third formant, fundamental frequency, sound range, fundamental frequency perturbation, first formant perturbation, third formant perturbation, and average energy of singing acoustic parameters. By using BP neural network methods, the quality of singing was evaluated objectively and compared with the subjective evaluation of senior vocal professional teachers. The results show that the BP neural network method can accurately and objectively evaluate the quality of singing voice by using the evaluation parameters, which is helpful in scientifically guiding the selection and training of artistic voice talents.
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47

Salazar, Randy, Rosario Cabillada, Mark Borres, and Anthony Jagures. "Fractal Dimensions of Voice Patterns and Voice Recognition." Recoletos Multidisciplinary Research Journal 2, no. 1 (June 30, 2014): 55–73. http://dx.doi.org/10.32871/rmrj1402.01.07.

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48

Baruch, Amit. "Voice control system with multiple voice recognition engines." Journal of the Acoustical Society of America 122, no. 3 (2007): 1322. http://dx.doi.org/10.1121/1.2781465.

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49

Oda, Mikio, and Tomoe Kawane. "Device for normalizing voice pitch for voice recognition." Journal of the Acoustical Society of America 121, no. 4 (2007): 1844. http://dx.doi.org/10.1121/1.2724039.

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50

Bommaji, Arun Pal. "Gender Voice Recognition Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2431–33. http://dx.doi.org/10.22214/ijraset.2022.44347.

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Abstract: Gender identification is one of the major problems in the area of signal processing. The system deals with finding the gender of a person using oral features. One of the most persnickety problems faced is feature selection from wide range of features, which is distinguishing factor in classifying the gender of aperson. The ideal of this design is to design a system that determines the speaker gender using the pitch of the speaker's voice. relating the gender from the plots of voice data set i.e., pitch, median, frequency etc. can be possible by using machine learning. In this design, we're trying to classify gender into male or female based on the data set containing varied attributes related to voice like pitch, frequency etc. The data set have features with explanation data points recorded samples of male and female voices. The data set can be trained with different machine learning algorithms. The proposed system can determine the gender of the speaker with real time test data a new result to discover the gender of the speaker using Fast Fourier Transform with Logistic Regression
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