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Artykuły w czasopismach na temat "Vocal recognition"
Guo, Taiyang, Zhi Zhu, Shunsuke Kidani i Masashi Unoki. "Contribution of Common Modulation Spectral Features to Vocal-Emotion Recognition of Noise-Vocoded Speech in Noisy Reverberant Environments". Applied Sciences 12, nr 19 (4.10.2022): 9979. http://dx.doi.org/10.3390/app12199979.
Pełny tekst źródłaSorokin, V. N., i I. S. Makarov. "Gender recognition from vocal source". Acoustical Physics 54, nr 4 (lipiec 2008): 571–78. http://dx.doi.org/10.1134/s1063771008040192.
Pełny tekst źródłaHuang, Chunyuan. "Vocal Music Teaching Pharyngeal Training Method Based on Audio Extraction by Big Data Analysis". Wireless Communications and Mobile Computing 2022 (6.05.2022): 1–11. http://dx.doi.org/10.1155/2022/4572904.
Pełny tekst źródłaMo, Wenwen, i Yuan Yuan. "Design of Interactive Vocal Guidance and Artistic Psychological Intervention System Based on Emotion Recognition". Occupational Therapy International 2022 (17.06.2022): 1–9. http://dx.doi.org/10.1155/2022/1079097.
Pełny tekst źródłaBryant, Gregory, i H. Clark Barrett. "Vocal Emotion Recognition Across Disparate Cultures". Journal of Cognition and Culture 8, nr 1-2 (2008): 135–48. http://dx.doi.org/10.1163/156770908x289242.
Pełny tekst źródłaMasapollo, Matthew, Linda Polka, Lucie Menard i Athena Vouloumanos. "Infant recognition of infant vocal signals". Journal of the Acoustical Society of America 133, nr 5 (maj 2013): 3334. http://dx.doi.org/10.1121/1.4805602.
Pełny tekst źródłaKonev, Anton, Evgeny Kostyuchenko i Alexey Yakimuk. "The program complex for vocal recognition". Journal of Physics: Conference Series 803 (styczeń 2017): 012077. http://dx.doi.org/10.1088/1742-6596/803/1/012077.
Pełny tekst źródłaSorokin, V. N., A. A. Tananykin i V. G. Trunov. "Speaker recognition using vocal source model". Pattern Recognition and Image Analysis 24, nr 1 (marzec 2014): 156–73. http://dx.doi.org/10.1134/s1054661814010179.
Pełny tekst źródłaSorokin, V. N. "Vocal Source Contribution to Speaker Recognition". Pattern Recognition and Image Analysis 28, nr 3 (lipiec 2018): 546–56. http://dx.doi.org/10.1134/s1054661818030197.
Pełny tekst źródłaHoude, Robert A., i James M. Hillenbrand. "Vocal tract normalization for vowel recognition". Journal of the Acoustical Society of America 121, nr 5 (maj 2007): 3189. http://dx.doi.org/10.1121/1.4782401.
Pełny tekst źródłaRozprawy doktorskie na temat "Vocal recognition"
Mathukumalli, Sravya. "Vocal combo android application". Kansas State University, 2016. http://hdl.handle.net/2097/32505.
Pełny tekst źródłaDepartment 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.
Pełny tekst źródła貴文, 古山, i 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.
Pełny tekst źródłaWildermoth, Brett Richard, i 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.
Pełny tekst źródłaWildermoth, Brett Richard. "Text-Independent Speaker Recognition Using Source Based Features". Thesis, Griffith University, 2001. http://hdl.handle.net/10072/366289.
Pełny tekst źródłaThesis (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.
Pełny tekst źródłaIdentity 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.
Pełny tekst źródłaYuditskaya, 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.
Pełny tekst źródłaCataloged 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.
Pełny tekst źródłaFace 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.
Pełny tekst źródłaKsiążki na temat "Vocal recognition"
Davé, Shilpa S. Introduction. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037405.003.0001.
Pełny tekst źródłaBrandzel, Amy L. Legal Detours of U.S. Empire. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040030.003.0004.
Pełny tekst źródłaProvine, Robert R. Beyond the Smile. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190613501.003.0011.
Pełny tekst źródłaSampson, Brett G., i Andrew D. Bersten. Therapeutic approach to bronchospasm and asthma. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0111.
Pełny tekst źródłaKayser, Casey. Marginalized. University Press of Mississippi, 2021. http://dx.doi.org/10.14325/mississippi/9781496835901.001.0001.
Pełny tekst źródłaCzęści książek na temat "Vocal recognition"
Gautier, Jean-Pierre, i Annie Gautier-Hion. "Vocal Quavering: A Basis for Recognition in Forest Guenons". W Primate Vocal Communication, 15–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73769-5_2.
Pełny tekst źródłaWang, Ning, P. C. Ching i Tan Lee. "Exploration of Phase and Vocal Excitation Modulation Features for Speaker Recognition". W Biometric Recognition, 251–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_31.
Pełny tekst źródłaKrothapalli, Sreenivasa Rao, i Shashidhar G. Koolagudi. "Emotion Recognition Using Vocal Tract Information". W 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.
Pełny tekst źródłaTholeti, Guru Sree Ram, Deepika Ghanta, N. V. S. Guru Sai Sarma Chilukuri i Shahana Bano. "Vocal Source Builds Divergence in Gender Recognition". W Lecture Notes in Electrical Engineering, 171–83. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3690-5_16.
Pełny tekst źródłaStoddard, Philip Kraft. "20. Vocal Recognition of Neighbors by Territorial Passerines". W Ecology and Evolution of Acoustic Communication in Birds, redaktorzy Donald E. Kroodsma i Edward H. Miller, 356–74. Ithaca, NY: Cornell University Press, 2020. http://dx.doi.org/10.7591/9781501736957-028.
Pełny tekst źródłaOuhnini, Ahmed, Brahim Aksasse i Mohammed Ouanan. "Vocal Parameters Analysis for Amazigh Phonemes Recognition System". W Digital Technologies and Applications, 43–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01942-5_5.
Pełny tekst źródłaZimmermann, Axel. "Artificial Neural Networks for Analysis and Recognition of Primate Vocal Communication". W 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.
Pełny tekst źródłaCharrier, Isabelle. "Mother–Offspring Vocal Recognition and Social System in Pinnipeds". W 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.
Pełny tekst źródłaAgarwal, Gaurav, Vikas Maheshkar, Sushila Maheshkar i Sachi Gupta. "Vocal Mood Recognition: Text Dependent Sequential and Parallel Approach". W Advances in Intelligent Systems and Computing, 131–42. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1819-1_14.
Pełny tekst źródłaAsawa, Krishna, i Raj Vardhan. "HMM Modeling of User Mood through Recognition of Vocal Emotions". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Vocal recognition"
Mayorga, P., i L. Besacier. "Voice over IP and Vocal Recognition". W 2006 3rd International Conference on Electrical and Electronics Engineering. IEEE, 2006. http://dx.doi.org/10.1109/iceee.2006.251861.
Pełny tekst źródłaMasapollo, Matthew, Linda Polka, Lucie Menard i Athena Vouloumanos. "Infant recognition of infant vocal signals". W ICA 2013 Montreal. ASA, 2013. http://dx.doi.org/10.1121/1.4798777.
Pełny tekst źródłaZhang, Yichi, i Zhiyao Duan. "Retrieving sounds by vocal imitation recognition". W 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015. http://dx.doi.org/10.1109/mlsp.2015.7324316.
Pełny tekst źródłaMurthy, Y. V. Srinivasa, Shashidhar G. Koolagudi i Vishnu G. Swaroop. "Vocal and Non-vocal Segmentation based on the Analysis of Formant Structure". W 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR). IEEE, 2017. http://dx.doi.org/10.1109/icapr.2017.8593164.
Pełny tekst źródłaHong, Alexander, Yuma Tsuboi, Goldie Nejat i Beno Benhabib. "Multimodal Affect Recognition for Assistive Human-Robot Interactions". W 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3332.
Pełny tekst źródłaAlghowinem, Sharifa, Roland Goecke, Julien Epps, Michael Wagner i Jeffrey Cohn. "Cross-Cultural Depression Recognition from Vocal Biomarkers". W Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1339.
Pełny tekst źródłaGu, Yu, Eric Postma i Hai-Xiang Lin. "Vocal Emotion Recognition with Log-Gabor Filters". W MM '15: ACM Multimedia Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808196.2811635.
Pełny tekst źródłaAsawa, Krishna, Vikrant Verma i Ankit Agrawal. "Recognition of vocal emotions from acoustic profile". W the International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2345396.2345512.
Pełny tekst źródłaHanchate, D. B., Mohini Nalawade, Manoj Pawar, Vijay Pophale i Prabhat Kumar Maurya. "Vocal digit recognition using Artificial Neural Network". W 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5486314.
Pełny tekst źródłaBonjyotsna, A., i M. Bhuyan. "Signal processing for segmentation of vocal and non-vocal regions in songs: A review". W 2013 International Conference on Signal Processing, Image Processing, and Pattern Recognition (ICSIPR). IEEE, 2013. http://dx.doi.org/10.1109/icsipr.2013.6497965.
Pełny tekst źródłaRaporty organizacyjne na temat "Vocal recognition"
Zhan, Puming, i Alex Waibel. Vocal Tract Length Normalization for Large Vocabulary Continuous Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, maj 1997. http://dx.doi.org/10.21236/ada333514.
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