Literatura académica sobre el tema "Conversion vocale"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Conversion vocale".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Conversion vocale"
Thamrin, Lily. "Phonological Description of Teochew Dialect in Pontianak West Kalimantan". Lingua Cultura 14, n.º 2 (30 de diciembre de 2020): 195–201. http://dx.doi.org/10.21512/lc.v14i2.6600.
Texto completoVevia Romero, Fernando Carlos. "Nostalgia de la conversación". Argos 6, n.º 17 (1 de enero de 2019): 149–57. http://dx.doi.org/10.32870/argos.v6.n19.14a19.
Texto completoHarris, Taran. "Treating Audio Manipulation Effects like Photoshop: Exploring the Negative Impacts of a Lack of Transparency in Contemporary Vocal Music on Young Learners". INSAM Journal of Contemporary Music, Art and Technology, n.º 8 (15 de julio de 2022): 47–59. http://dx.doi.org/10.51191/issn.2637-1898.2022.5.8.47.
Texto completoNishimura, Shogo, Takuya Nakamura, Wataru Sato, Masayuki Kanbara, Yuichiro Fujimoto, Hirokazu Kato y Norihiro Hagita. "Vocal Synchrony of Robots Boosts Positive Affective Empathy". Applied Sciences 11, n.º 6 (11 de marzo de 2021): 2502. http://dx.doi.org/10.3390/app11062502.
Texto completoNirmal, Jagannath, Suprava Patnaik, Mukesh Zaveri y Pramod Kachare. "Complex Cepstrum Based Voice Conversion Using Radial Basis Function". ISRN Signal Processing 2014 (6 de febrero de 2014): 1–13. http://dx.doi.org/10.1155/2014/357048.
Texto completoZeitels, Steven M., Ramon A. Franco, Robert E. Hillman y Glenn W. Bunting. "Voice and Treatment Outcome from Phonosurgical Management of Early Glottic Cancer". Annals of Otology, Rhinology & Laryngology 111, n.º 12_suppl (diciembre de 2002): 3–20. http://dx.doi.org/10.1177/0003489402111s1202.
Texto completoAdachi, Seiji, Hironori Takemoto, Tatsuya Kitamura, Parham Mokhtari y Kiyoshi Honda. "Vocal tract length perturbation and its application to male-female vocal tract shape conversion". Journal of the Acoustical Society of America 121, n.º 6 (junio de 2007): 3874–85. http://dx.doi.org/10.1121/1.2730743.
Texto completoVijayan, Karthika, Haizhou Li y Tomoki Toda. "Speech-to-Singing Voice Conversion: The Challenges and Strategies for Improving Vocal Conversion Processes". IEEE Signal Processing Magazine 36, n.º 1 (enero de 2019): 95–102. http://dx.doi.org/10.1109/msp.2018.2875195.
Texto completoTreinkman, Melissa. "A Conversation with Leslie Holmes". Journal of Singing 80, n.º 1 (15 de agosto de 2023): 89–91. http://dx.doi.org/10.53830/tfcq4189.
Texto completoGEIST, ROSE y SUSAN E. TALLETT. "Diagnosis and Management of Psychogenic Stridor Caused by a Conversion Disorder". Pediatrics 86, n.º 2 (1 de agosto de 1990): 315–17. http://dx.doi.org/10.1542/peds.86.2.315.
Texto completoTesis sobre el tema "Conversion vocale"
Huber, Stefan. "Voice Conversion by modelling and transformation of extended voice characteristics". Electronic Thesis or Diss., Paris 6, 2015. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2015PA066750.pdf.
Texto completoVoice Conversion (VC) aims at transforming the characteristics of a source speaker’s voice in such a way that it will be perceived as being uttered by a target speaker. The principle of VC is to define mapping functions for the conversion from one source speaker’s voice to one target speaker’s voice. The transformation functions of common State-Of-The-Art (START) VC system adapt instantaneously to the characteristics of the source voice. While recent VC systems have made considerable progress over the conversion quality of initial approaches, the quality is nevertheless not yet sufficient. Considerable improvements are required before VC techniques can be used in an professional industrial environment. The objective of this thesis is to augment the quality of Voice Conversion to facilitate its industrial applicability to a reasonable extent. The basic properties of different START algorithms for Voice Conversion are discussed on their intrinsic advantages and shortcomings. Based on experimental evaluations of one GMM-based State-Of-The-Art VC approach the conclusion is that most VC systems which rely on statistical models are, due to averaging effect of the linear regression, less appropriate to achieve a high enough similarity score to the target speaker required for industrial usage. The contributions established throughout this thesis work lie in the extended means to a) model the glottal excitation source, b) model a voice descriptor set using a novel speech system based on an extended source-filter model, and c) to further advance IRCAM’s novel VC system by combining it with the contributions of a) and b)
Guéguin, Marie. "Evaluation objective de la qualité vocale en contexte de conversation". Phd thesis, Université Rennes 1, 2006. http://tel.archives-ouvertes.fr/tel-00132550.
Texto completoGuéguin, Marie. "Évaluation objective de la qualité vocale en contexte de conversation". Rennes 1, 2006. https://tel.archives-ouvertes.fr/tel-00132550.
Texto completoOgun, Sewade. "Generating diverse synthetic data for ASR training data augmentation". Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0116.
Texto completoIn the last two decades, the error rate of automatic speech recognition (ASR) systems has drastically dropped, making them more useful in real-world applications. This improvement can be attributed to several factors including new architectures using deep learning techniques, new training algorithms, large and diverse training datasets, and data augmentation. In particular, the large-scale training datasets have been pivotal to learning robust speech representations for ASR. Their large size allows them to effectively cover the inherent diversity in speech, in terms of speaker voice, speaking rate, pitch, reverberation, and noise. However, the size and diversity of datasets typically found in high-resourced languages are not available in medium- and low-resourced languages and in domains with specialised vocabulary like the medical domain. Therefore, the popular method to increase dataset diversity is through data augmentation. With the recent increase in the naturalness and quality of synthetic data that can be generated by text-to-speech (TTS) and voice conversion (VC) systems, these systems have also become viable options for ASR data augmentation. However, several problems limit their application. First, TTS/VC systems require high-quality speech data for training. Hence, we develop a method of dataset curation from an ASR-designed corpus for training a TTS system. This method leverages the increasing accuracy of deep-learning-based, non-intrusive quality estimators to filter high-quality samples. We explore filtering the ASR dataset at different thresholds to balance the size of the dataset, number of speakers, and quality. With this method, we create a high-quality multi-speaker dataset which is comparable to LibriTTS in quality. Second, the data generation process needs to be controllable to generate diverse TTS/VC data with specific attributes. Previous TTS/VC systems either condition the system on the speaker embedding alone or use discriminative models to learn the speech variabilities. In our approach, we design an improved flow-based architecture that learns the distribution of different speech variables. We find that our modifications significantly increase the diversity and naturalness of the generated utterances over a GlowTTS baseline, while being controllable. Lastly, we evaluated the significance of generating diverse TTS and VC data for augmenting ASR training data. As opposed to naively generating the TTS/VC data, we independently examined different approaches such as sentence selection methods and increasing the diversity of speakers, phoneme duration, and pitch contours, in addition to systematically increasing the environmental conditions of the generated data. Our results show that TTS/VC augmentation holds promise in increasing ASR performance in low- and medium-data regimes. In conclusion, our experiments provide insight into the variabilities that are particularly important for ASR, and reveal a systematic approach to ASR data augmentation using synthetic data
Berger, Israel. "Inaction and silent action in interaction". Thesis, University of Roehampton, 2013. https://pure.roehampton.ac.uk/portal/en/studentthesis/inaction-and-silent-action-in-interaction(a49cedf3-0263-463f-9362-12e13ad2f6e9).html.
Texto completoHowell, Ashley N. "Effects of Social Context on State Anxiety, Submissive Behavior, and Perceived Social Task Performance in Females with Social Anxiety". Ohio University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1365441706.
Texto completoDeschamps-Berger, Théo. "Social Emotion Recognition with multimodal deep learning architecture in emergency call centers". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG036.
Texto completoThis thesis explores automatic speech-emotion recognition systems in a medical emergency context. It addresses some of the challenges encountered when studying emotions in social interactions. It is rooted in modern theories of emotions, particularly those of Lisa Feldman Barrett on the construction of emotions. Indeed, the manifestation of emotions in human interactions is complex and often characterized by nuanced, mixed, and is highly linked to the context. This study is based on the CEMO corpus, which is composed of telephone conversations between callers and emergency medical dispatchers (EMD) from a French emergency call center. This corpus provides a rich dataset to explore the capacity of deep learning systems, such as Transformers and pre-trained models, to recognize spontaneous emotions in spoken interactions. The applications could be to provide emotional cues that could improve call handling and decision-making by EMD, or to summarize calls. The work carried out in my thesis focused on different techniques related to speech emotion recognition, including transfer learning from pre-trained models, multimodal fusion strategies, dialogic context integration, and mixed emotion detection. An initial acoustic system based on temporal convolutions and recurrent networks was developed and validated on an emotional corpus widely used by the affective community, called IEMOCAP, and then on the CEMO corpus. Extensive research on multimodal systems, pre-trained in acoustics and linguistics and adapted to emotion recognition, is presented. In addition, the integration of dialog context in emotion recognition was explored, underlining the complex dynamics of emotions in social interactions. Finally, research has been initiated towards developing multi-label, multimodal systems capable of handling the subtleties of mixed emotions, often due to the annotator's perception and social context. Our research highlights some solutions and challenges in recognizing emotions in the wild. The CNRS AI HUMAAINE Chair: HUman-MAchine Affective Interaction & Ethics funded this thesis
"Conversation, Dark haze, San-shui Xi-nan". 1998. http://library.cuhk.edu.hk/record=b5896306.
Texto completoThesis (M.Mus.)--Chinese University of Hong Kong, 1998.
Abstract also in Chinese.
Chapter Part I: --- p.page
Chapter ´Ø --- Abstract --- p.1
Chapter Part II:
Chapter ´Ø --- "Analysis on ""Conversation""" --- p.3
Chapter ´Ø --- """Conversation"" (Full Score)" --- p.6
Chapter ´Ø --- "Analysis on ""Dark Haze´ح" --- p.25
Chapter ´Ø --- """Dark Haze"" (Full Score)" --- p.28
Chapter ´Ø --- "Analysis on ""San-Shui Xi-Nan""" --- p.65
Chapter ´Ø --- """San -Shui Xi-Nan"" (Full Score)" --- p.69
Chapter Part III:
Chapter ´Ø --- Biography --- p.119
Libros sobre el tema "Conversion vocale"
Klein, Evelyn R., Cesar E. Ruiz y Louis R. Chesney. Echo: A Vocal Language Program for Building Ease and Comfort with Conversation. Plural Publishing, Incorporated, 2021.
Buscar texto completoEidsheim, Nina Sun y Katherine Meizel, eds. The Oxford Handbook of Voice Studies. Oxford University Press, 2019. http://dx.doi.org/10.1093/oxfordhb/9780199982295.001.0001.
Texto completoBarnard, Stephen R. Hacking Hybrid Media. Oxford University PressNew York, 2024. http://dx.doi.org/10.1093/oso/9780197570272.001.0001.
Texto completoBudney, Stephen. William Jay. Greenwood Publishing Group, Inc., 2005. http://dx.doi.org/10.5040/9798216035947.
Texto completoCapítulos de libros sobre el tema "Conversion vocale"
Vekkot, Susmitha y Shikha Tripathi. "Vocal Emotion Conversion Using WSOLA and Linear Prediction". En Speech and Computer, 777–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66429-3_78.
Texto completoVekkot, Susmitha y Shikha Tripathi. "Significance of Glottal Closure Instants Detection Algorithms in Vocal Emotion Conversion". En Soft Computing Applications, 462–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62521-8_40.
Texto completoTeo, Nicole, Zhaoxia Wang, Ezekiel Ghe, Yee Sen Tan, Kevan Oktavio, Alexander Vincent Lewi, Allyne Zhang y Seng-Beng Ho. "DLVS4Audio2Sheet: Deep Learning-Based Vocal Separation for Audio into Music Sheet Conversion". En Lecture Notes in Computer Science, 95–107. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2650-9_8.
Texto completoMatthews, Colin. "Un Colloque sentimental (A Sentimental Conversation)". En New Vocal Repertory 2, 172–79. Oxford University PressOxford, 1998. http://dx.doi.org/10.1093/oso/9780198790181.003.0038.
Texto completoJuslin, Patrik N. y Klaus R. Scherer. "Vocal expression of affect". En The New Handbook of Methods in Nonverbal Behavior Research, 65–136. Oxford University PressOxford, 2005. http://dx.doi.org/10.1093/oso/9780198529613.003.0003.
Texto completoMcnally, Michael D. "Ojibwes, Missionaries, And Hymn Singing, 1828-1867". En Ojibwe Singers, 43–80. Oxford University PressNew York, NY, 2000. http://dx.doi.org/10.1093/oso/9780195134643.003.0003.
Texto completoRecasens, Daniel. "Velar palatalization". En Phonetic Causes of Sound Change, 22–76. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198845010.003.0003.
Texto completo"“A Little Singer on Broadway”". En Blues Mamas and Broadway Belters, 106–61. Duke University Press, 2024. http://dx.doi.org/10.1215/9781478059967-004.
Texto completoSchneider, Magnus Tessing. "From the General to the Specific: The Musical Director’s Perspective". En Performing the Eighteenth Century: Theatrical Discourses, Practices, and Artefacts, 225–34. Stockholm University Press, 2023. http://dx.doi.org/10.16993/bce.k.
Texto completo"“TO CHANGE THE ORDER OF CONVERSATION”: interruption and vocal diversity in Holmes' American talk". En Oliver Wendell Holmes and the Culture of Conversation, 61–90. Cambridge University Press, 2001. http://dx.doi.org/10.1017/cbo9780511485503.003.
Texto completoActas de conferencias sobre el tema "Conversion vocale"
Chan, Paul Y., Minghui Dong, S. W. Lee y Ling Cen. "Solo to a capella conversion - Synthesizing vocal harmony from lead vocals". En 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6012032.
Texto completoLiliana, Resmana Lim y Elizabeth Kwan. "Voice conversion application (VOCAL)". En 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering (URKE). IEEE, 2011. http://dx.doi.org/10.1109/urke.2011.6007812.
Texto completoRao, K. Sreenivasa y B. Yegnanarayana. "Voice Conversion by Prosody and Vocal Tract Modification". En 9th International Conference on Information Technology (ICIT'06). IEEE, 2006. http://dx.doi.org/10.1109/icit.2006.92.
Texto completoVekkot, Susmitha. "Building a generalized model for multi-lingual vocal emotion conversion". En 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2017. http://dx.doi.org/10.1109/acii.2017.8273658.
Texto completoTurk, Oytun y Levent M. Arslan. "Voice conversion methods for vocal tract and pitch contour modification". En 8th European Conference on Speech Communication and Technology (Eurospeech 2003). ISCA: ISCA, 2003. http://dx.doi.org/10.21437/eurospeech.2003-36.
Texto completoWeichao, Xie y Zhang Linghua. "Vocal tract spectrum transformation based on clustering in voice conversion system". En 2012 International Conference on Information and Automation (ICIA). IEEE, 2012. http://dx.doi.org/10.1109/icinfa.2012.6246812.
Texto completoNikolay, Korotaev. "Collaborative constructions in Russian conversations: A multichannel perspective". En INTERNATIONAL CONFERENCE on Computational Linguistics and Intellectual Technologies. RSUH, 2023. http://dx.doi.org/10.28995/2075-7182-2023-22-254-266.
Texto completoShah, Nirmesh, Maulik C. Madhavi y Hemant Patil. "Unsupervised Vocal Tract Length Warped Posterior Features for Non-Parallel Voice Conversion". En Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1712.
Texto completoSaito, Daisuke, Satoshi Asakawa, Nobuaki Minematsu y Keikichi Hirose. "Structure to speech conversion - speech generation based on infant-like vocal imitation". En Interspeech 2008. ISCA: ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-178.
Texto completoZhu, Zhi, Ryota Miyauchi, Yukiko Araki y Masashi Unoki. "Feasibility of vocal emotion conversion on modulation spectrogram for simulated cochlear implants". En 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081526.
Texto completo