Academic literature on the topic 'Vocal recognition'
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Journal articles on the topic "Vocal recognition"
Guo, Taiyang, Zhi Zhu, Shunsuke Kidani, and Masashi Unoki. "Contribution of Common Modulation Spectral Features to Vocal-Emotion Recognition of Noise-Vocoded Speech in Noisy Reverberant Environments." Applied Sciences 12, no. 19 (October 4, 2022): 9979. http://dx.doi.org/10.3390/app12199979.
Full textSorokin, V. N., and I. S. Makarov. "Gender recognition from vocal source." Acoustical Physics 54, no. 4 (July 2008): 571–78. http://dx.doi.org/10.1134/s1063771008040192.
Full textHuang, Chunyuan. "Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/4572904.
Full textMo, Wenwen, and Yuan Yuan. "Design of Interactive Vocal Guidance and Artistic Psychological Intervention System Based on Emotion Recognition." Occupational Therapy International 2022 (June 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/1079097.
Full textBryant, Gregory, and H. Clark Barrett. "Vocal Emotion Recognition Across Disparate Cultures." Journal of Cognition and Culture 8, no. 1-2 (2008): 135–48. http://dx.doi.org/10.1163/156770908x289242.
Full textMasapollo, Matthew, Linda Polka, Lucie Menard, and Athena Vouloumanos. "Infant recognition of infant vocal signals." Journal of the Acoustical Society of America 133, no. 5 (May 2013): 3334. http://dx.doi.org/10.1121/1.4805602.
Full textKonev, Anton, Evgeny Kostyuchenko, and Alexey Yakimuk. "The program complex for vocal recognition." Journal of Physics: Conference Series 803 (January 2017): 012077. http://dx.doi.org/10.1088/1742-6596/803/1/012077.
Full textSorokin, V. N., A. A. Tananykin, and V. G. Trunov. "Speaker recognition using vocal source model." Pattern Recognition and Image Analysis 24, no. 1 (March 2014): 156–73. http://dx.doi.org/10.1134/s1054661814010179.
Full textSorokin, V. N. "Vocal Source Contribution to Speaker Recognition." Pattern Recognition and Image Analysis 28, no. 3 (July 2018): 546–56. http://dx.doi.org/10.1134/s1054661818030197.
Full textHoude, Robert A., and James M. Hillenbrand. "Vocal tract normalization for vowel recognition." Journal of the Acoustical Society of America 121, no. 5 (May 2007): 3189. http://dx.doi.org/10.1121/1.4782401.
Full textDissertations / Theses on the topic "Vocal recognition"
Mathukumalli, Sravya. "Vocal combo android application." Kansas State University, 2016. http://hdl.handle.net/2097/32505.
Full textDepartment of Computing and Information Sciences
Mitchell L. Neilsen
Now-a-days people from various backgrounds need different information on demand. People are relying on web as a source of information they need. But to get connected to internet all the time with a computer system is not possible. Android, being open source has already made its mark in the mobile application development. The highest smart phone user base and the ease of developing the applications in Android is an added advantage for the users as well as the Android developers. The vocal combo is an Android application which provides the required functionality on Android supported smart phone or a tablet. This provides the flexibility of accessing information at the users’ fingertips. This application is built using Android SDK, which makes the application easy to deploy on any Android powered device. Vocal Combo is a combination of voice based applications. It includes a Text-To-Voice convertor and Voice-To-Text convertor. This application helps the user to learn the pronunciation of various words. At the same time the user can also check his/her pronunciation skills. This application also provides the functionality of meaning check where the user can check the meaning of the words he types in or speaks out. At any point of time, the user can check the history of the words for which he has checked the meaning or pronunciation for. The application also provides the support to the user on how to use this application.
Benkrid, A. "Real time TLM vocal tract modelling." Thesis, University of Nottingham, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.352958.
Full text貴文, 古山, and Takafumi Furuyama. "Vocal recognition in primate : comparison between Japanese macaques and humans." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13045021/?lang=0, 2017. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB13045021/?lang=0.
Full textWildermoth, Brett Richard, and n/a. "Text-Independent Speaker Recognition Using Source Based Features." Griffith University. School of Microelectronic Engineering, 2001. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20040831.115646.
Full textWildermoth, Brett Richard. "Text-Independent Speaker Recognition Using Source Based Features." Thesis, Griffith University, 2001. http://hdl.handle.net/10072/366289.
Full textThesis (Masters)
Master of Philosophy (MPhil)
School of Microelectronic Engineering
Faculty of Engineering and Information Technology
Full Text
Keenan, Sumir. "Identity information in bonobo vocal communication : from sender to receiver." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES038/document.
Full textIdentity information is vital for highly social species as it facilitates individual recognition and allows for differentiation between social partners in many contexts, such as dominance hierarchies, territorial defence, mating and parent-offspringidentification and group cohesion and coordination. In many species vocalisations can be the most effective communication channel through complex environments and over long-distances and are encoded with the stable features of an individual’s voice. Associations between these individual vocal signatures and accumulated social knowledge about conspecifics can greatly increase an animal’s fitness, as it facilitates adaptively constructive social decisions. This thesis investigates the encoding and decoding of identity information in the vocal communication system of the bonobo, Pan paniscus. We firstly investigated the stability of vocal signatures across the five most common call types in the bonobo vocal repertoire. Results showed that while all call types have the potential to code identity information, loud calls used during times of high arousal and for distance communication have the strongest individual vocal signatures. Following the first study, we investigated if social familiarity and relatedness affect the acoustic features that code individual information in the bark call type. Overall, we found strong evidence for vocal convergence, and specifically, that individuals who are related and familiar, independently from one another, are more vocally similar to one another than unrelated and unfamiliar individuals. In a final study we tested if bonobos are capable of using the encoded identity information to recognise past group members that they no longer live with. Through a series playback experiments we demonstrated that bonobos are capable of recognising familiar individuals from vocalisations alone even after years of separation. Collectively, the results of this thesis show that the encoding and decoding of identity information in bonobo vocalisations is a dynamic system, subject to modification through social processes but robust enough to allow for individual recognition over time. In conclusion these studies contribute to a better understanding of the vocal communication system of a non-human primate species with a unique and complex social network
Ma, Zongqiang. "Spontaneous speech recognition using statistical dynamic models for the vocal-tract-resonance dynamics." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0020/NQ53993.pdf.
Full textYuditskaya, Sophia. "Automatic vocal recognition of a child's perceived emotional state within the Speechome corpus." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62086.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 137-149).
With over 230,000 hours of audio/video recordings of a child growing up in the home setting from birth to the age of three, the Human Speechome Project has pioneered a comprehensive, ecologically valid observational dataset that introduces far-reaching new possibilities for the study of child development. By offering In vivo observation of a child's daily life experience at ultra-dense, longitudinal time scales, the Speechome corpus holds great potential for discovering developmental insights that have thus far eluded observation. The work of this thesis aspires to enable the use of the Speechome corpus for empirical study of emotional factors in early child development. To fully harness the benefits of Speechome for this purpose, an automated mechanism must be created to perceive the child's emotional state within this medium. Due to the latent nature of emotion, we sought objective, directly measurable correlates of the child's perceived emotional state within the Speechome corpus, focusing exclusively on acoustic features of the child's vocalizations and surrounding caretaker speech. Using Partial Least Squares regression, we applied these features to build a model that simulates human perceptual heuristics for determining a child's emotional state. We evaluated the perceptual accuracy of models built across child-only, adult-only, and combined feature sets within the overall sampled dataset, as well as controlling for social situations, vocalization behaviors (e.g. crying, laughing, babble), individual caretakers, and developmental age between 9 and 24 months. Child and combined models consistently demonstrated high perceptual accuracy, with overall adjusted R-squared values of 0.54 and 0.58, respectively, and an average of 0.59 and 0.67 per month. Comparative analysis across longitudinal and socio-behavioral contexts yielded several notable developmental and dyadic insights. In the process, we have developed a data mining and analysis methodology for modeling perceived child emotion and quantifying caretaker intersubjectivity that we hope to extend to future datasets across multiple children, as new deployments of the Speechome recording technology are established. Such large-scale comparative studies promise an unprecedented view into the nature of emotional processes in early childhood and potentially enlightening discoveries about autism and other developmental disorders.
by Sophia Yuditskaya.
S.M.
Schall, Sonja. "The face in your voice–how audiovisual learning benefits vocal communication." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2014. http://dx.doi.org/10.18452/17039.
Full textFace and voice of a person are strongly associated with each other and usually perceived as a single entity. Despite the natural co-occurrence of faces and voices, brain research has traditionally approached their perception from a unisensory perspective. This means that research into face perception has exclusively focused on the visual system, while research into voice perception has exclusively probed the auditory system. In this thesis, I suggest that the brain has adapted to the multisensory nature of faces and voices and that this adaptation is evident even when one input stream is missing, that is, when input is actually unisensory. Specifically, the current work investigates how the brain exploits previously learned voice-face associations to optimize the auditory processing of voices and vocal speech. Three empirical studies providing spatiotemporal brain data—via functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG)—constitute this thesis. All data were acquired while participants listened to auditory-only speech samples of previously familiarized speakers (with or without seeing the speakers’ faces). Three key findings demonstrate that previously learned visual speaker information support the auditory analysis of vocal sounds: (i) face-sensitive areas were part of the sensory network activated by voices, (ii) the auditory analysis of voices was temporally facilitated by learned facial associations and (iii) multisensory interactions between face- and voice/speech-sensitive regions were increased. The current work challenges traditional unisensory views on vocal perception and rather suggests that voice and vocal speech perception profit from a multisensory neural processing scheme.
Garau, Giulia. "Speaker normalisation for large vocabulary multiparty conversational speech recognition." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3983.
Full textBooks on the topic "Vocal recognition"
Davé, Shilpa S. Introduction. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037405.003.0001.
Full textBrandzel, Amy L. Legal Detours of U.S. Empire. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040030.003.0004.
Full textProvine, Robert R. Beyond the Smile. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0011.
Full textSampson, Brett G., and Andrew D. Bersten. Therapeutic approach to bronchospasm and asthma. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0111.
Full textKayser, Casey. Marginalized. University Press of Mississippi, 2021. http://dx.doi.org/10.14325/mississippi/9781496835901.001.0001.
Full textBook chapters on the topic "Vocal recognition"
Gautier, Jean-Pierre, and Annie Gautier-Hion. "Vocal Quavering: A Basis for Recognition in Forest Guenons." In Primate Vocal Communication, 15–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73769-5_2.
Full textWang, Ning, P. C. Ching, and Tan Lee. "Exploration of Phase and Vocal Excitation Modulation Features for Speaker Recognition." In Biometric Recognition, 251–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_31.
Full textKrothapalli, Sreenivasa Rao, and Shashidhar G. Koolagudi. "Emotion Recognition Using Vocal Tract Information." In SpringerBriefs in Electrical and Computer Engineering, 67–78. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-5143-3_4.
Full textTholeti, Guru Sree Ram, Deepika Ghanta, N. V. S. Guru Sai Sarma Chilukuri, and Shahana Bano. "Vocal Source Builds Divergence in Gender Recognition." In Lecture Notes in Electrical Engineering, 171–83. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3690-5_16.
Full textStoddard, Philip Kraft. "20. Vocal Recognition of Neighbors by Territorial Passerines." In Ecology and Evolution of Acoustic Communication in Birds, edited by Donald E. Kroodsma and Edward H. Miller, 356–74. Ithaca, NY: Cornell University Press, 2020. http://dx.doi.org/10.7591/9781501736957-028.
Full textOuhnini, Ahmed, Brahim Aksasse, and Mohammed Ouanan. "Vocal Parameters Analysis for Amazigh Phonemes Recognition System." In Digital Technologies and Applications, 43–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01942-5_5.
Full textZimmermann, Axel. "Artificial Neural Networks for Analysis and Recognition of Primate Vocal Communication." In Current Topics in Primate Vocal Communication, 29–46. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-9930-9_2.
Full textCharrier, Isabelle. "Mother–Offspring Vocal Recognition and Social System in Pinnipeds." In Coding Strategies in Vertebrate Acoustic Communication, 231–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39200-0_9.
Full textAgarwal, Gaurav, Vikas Maheshkar, Sushila Maheshkar, and Sachi Gupta. "Vocal Mood Recognition: Text Dependent Sequential and Parallel Approach." In Advances in Intelligent Systems and Computing, 131–42. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1819-1_14.
Full textAsawa, Krishna, and Raj Vardhan. "HMM Modeling of User Mood through Recognition of Vocal Emotions." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 229–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36642-0_23.
Full textConference papers on the topic "Vocal recognition"
Mayorga, P., and L. Besacier. "Voice over IP and Vocal Recognition." In 2006 3rd International Conference on Electrical and Electronics Engineering. IEEE, 2006. http://dx.doi.org/10.1109/iceee.2006.251861.
Full textMasapollo, Matthew, Linda Polka, Lucie Menard, and Athena Vouloumanos. "Infant recognition of infant vocal signals." In ICA 2013 Montreal. ASA, 2013. http://dx.doi.org/10.1121/1.4798777.
Full textZhang, Yichi, and Zhiyao Duan. "Retrieving sounds by vocal imitation recognition." In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015. http://dx.doi.org/10.1109/mlsp.2015.7324316.
Full textMurthy, Y. V. Srinivasa, Shashidhar G. Koolagudi, and Vishnu G. Swaroop. "Vocal and Non-vocal Segmentation based on the Analysis of Formant Structure." In 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, 2017. http://dx.doi.org/10.1109/icapr.2017.8593164.
Full textHong, Alexander, Yuma Tsuboi, Goldie Nejat, and Beno Benhabib. "Multimodal Affect Recognition for Assistive Human-Robot Interactions." In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3332.
Full textAlghowinem, Sharifa, Roland Goecke, Julien Epps, Michael Wagner, and Jeffrey Cohn. "Cross-Cultural Depression Recognition from Vocal Biomarkers." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1339.
Full textGu, Yu, Eric Postma, and Hai-Xiang Lin. "Vocal Emotion Recognition with Log-Gabor Filters." In MM '15: ACM Multimedia Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808196.2811635.
Full textAsawa, Krishna, Vikrant Verma, and Ankit Agrawal. "Recognition of vocal emotions from acoustic profile." In the International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345512.
Full textHanchate, D. B., Mohini Nalawade, Manoj Pawar, Vijay Pophale, and Prabhat Kumar Maurya. "Vocal digit recognition using Artificial Neural Network." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5486314.
Full textBonjyotsna, A., and M. Bhuyan. "Signal processing for segmentation of vocal and non-vocal regions in songs: A review." In 2013 International Conference on Signal Processing, Image Processing, and Pattern Recognition (ICSIPR). IEEE, 2013. http://dx.doi.org/10.1109/icsipr.2013.6497965.
Full textReports on the topic "Vocal recognition"
Zhan, Puming, and Alex Waibel. Vocal Tract Length Normalization for Large Vocabulary Continuous Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, May 1997. http://dx.doi.org/10.21236/ada333514.
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