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You searched for +publisher:"University of New South Wales" +contributor:("Sethu, Vidhyasaharan, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW"). Showing records 1 – 7 of 7 total matches.

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University of New South Wales

1. Wataraka Gamage, Kalani. Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems.

Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales

 Speech-based emotion recognition is a research field of growing interest, which aims to identifyhuman emotions based on speech. The main contributions of this thesis revolve… (more)

Subjects/Keywords: Emotion classification; Speech based Emotion Recognition; Continuous Emotion Prediction; Vocal Gestures; Non-verbal vocalizations; Lexical

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APA (6th Edition):

Wataraka Gamage, K. (2018). Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Wataraka Gamage, Kalani. “Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems.” 2018. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true.

MLA Handbook (7th Edition):

Wataraka Gamage, Kalani. “Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems.” 2018. Web. 02 Mar 2021.

Vancouver:

Wataraka Gamage K. Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true.

Council of Science Editors:

Wataraka Gamage K. Speech-Based Emotion Recognition: Linguistic and Saliency-Based Systems. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60426 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:52192/SOURCE02?view=true


University of New South Wales

2. Cummins, Nicholas. Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning.

Degree: Electrical Engineering & Telecommunications, 2016, University of New South Wales

 Clinical depression is a prominent cause of disability and burden worldwide. Despite this prevalence, the diagnosis of depression, due to its complex clinical characterisation, is… (more)

Subjects/Keywords: Paralinguistic cues; Acoustic Volume measures; Probabilistic Acoustic Volume (PAV); Acoustic models

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APA (6th Edition):

Cummins, N. (2016). Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/55642 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:38198/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Cummins, Nicholas. “Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning.” 2016. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/55642 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:38198/SOURCE02?view=true.

MLA Handbook (7th Edition):

Cummins, Nicholas. “Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning.” 2016. Web. 02 Mar 2021.

Vancouver:

Cummins N. Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning. [Internet] [Doctoral dissertation]. University of New South Wales; 2016. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/55642 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:38198/SOURCE02?view=true.

Council of Science Editors:

Cummins N. Automatic assessment of depression from speech: paralinguistic analysis, modelling and machine learning. [Doctoral Dissertation]. University of New South Wales; 2016. Available from: http://handle.unsw.edu.au/1959.4/55642 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:38198/SOURCE02?view=true


University of New South Wales

3. Brown, Stefanie. Analysis and minimisation of aliasing and truncation errors in the extraction of soundfields using spherical microphone arrays.

Degree: Electrical Engineering & Telecommunications, 2017, University of New South Wales

 The spherical harmonic (SH) framework is a powerful representation that can be used to describe to a 3D soundfield. It decomposes waves propagating through space… (more)

Subjects/Keywords: Truncation error; Spherical microphone array; Spatial aliasing; spatial Wiener filter

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APA (6th Edition):

Brown, S. (2017). Analysis and minimisation of aliasing and truncation errors in the extraction of soundfields using spherical microphone arrays. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/59104 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:48464/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Brown, Stefanie. “Analysis and minimisation of aliasing and truncation errors in the extraction of soundfields using spherical microphone arrays.” 2017. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/59104 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:48464/SOURCE02?view=true.

MLA Handbook (7th Edition):

Brown, Stefanie. “Analysis and minimisation of aliasing and truncation errors in the extraction of soundfields using spherical microphone arrays.” 2017. Web. 02 Mar 2021.

Vancouver:

Brown S. Analysis and minimisation of aliasing and truncation errors in the extraction of soundfields using spherical microphone arrays. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/59104 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:48464/SOURCE02?view=true.

Council of Science Editors:

Brown S. Analysis and minimisation of aliasing and truncation errors in the extraction of soundfields using spherical microphone arrays. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/59104 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:48464/SOURCE02?view=true


University of New South Wales

4. Irtza, Saad. Scalable Hierarchical Language Identification System.

Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales

 Humans’ speech carries a great deal of information including linguistic contents (i.e. what is being said), speaker identity, language spoken, gender and emotions. The capacity… (more)

Subjects/Keywords: Language Clustering; Spoken Language Identification; Hierarchical Structure; Deep Neural Network, CNN, LSTM; End-to-End Language Identification System

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APA (6th Edition):

Irtza, S. (2018). Scalable Hierarchical Language Identification System. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60018 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51118/SOURCE2?view=true

Chicago Manual of Style (16th Edition):

Irtza, Saad. “Scalable Hierarchical Language Identification System.” 2018. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/60018 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51118/SOURCE2?view=true.

MLA Handbook (7th Edition):

Irtza, Saad. “Scalable Hierarchical Language Identification System.” 2018. Web. 02 Mar 2021.

Vancouver:

Irtza S. Scalable Hierarchical Language Identification System. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/60018 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51118/SOURCE2?view=true.

Council of Science Editors:

Irtza S. Scalable Hierarchical Language Identification System. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60018 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51118/SOURCE2?view=true


University of New South Wales

5. Dang, Ting. Speech based Continuous Emotion Prediction: An investigation of Speaker Variability and Emotion Uncertainty.

Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales

 Understanding and describing human emotional state is important for many applications such as interactive human-computer interface design and clinical diagnosis tools. Speech based emotion prediction… (more)

Subjects/Keywords: pattern recognition; speech processing; machine learning

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APA (6th Edition):

Dang, T. (2018). Speech based Continuous Emotion Prediction: An investigation of Speaker Variability and Emotion Uncertainty. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60161 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51221/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Dang, Ting. “Speech based Continuous Emotion Prediction: An investigation of Speaker Variability and Emotion Uncertainty.” 2018. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/60161 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51221/SOURCE02?view=true.

MLA Handbook (7th Edition):

Dang, Ting. “Speech based Continuous Emotion Prediction: An investigation of Speaker Variability and Emotion Uncertainty.” 2018. Web. 02 Mar 2021.

Vancouver:

Dang T. Speech based Continuous Emotion Prediction: An investigation of Speaker Variability and Emotion Uncertainty. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/60161 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51221/SOURCE02?view=true.

Council of Science Editors:

Dang T. Speech based Continuous Emotion Prediction: An investigation of Speaker Variability and Emotion Uncertainty. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60161 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51221/SOURCE02?view=true


University of New South Wales

6. Sriskandaraja, Kaavya. Spoofing countermeasures for secure and robust voice authentication system: Feature extraction and modelling.

Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales

 The ability to employ automatic speaker verification systems without face-to-face contact makes them more prone to spoofing attacks compared to other biometric systems. The study… (more)

Subjects/Keywords: Spoofing countermeasures; Speech Processing; Speaker verification; Voice authentication

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APA (6th Edition):

Sriskandaraja, K. (2018). Spoofing countermeasures for secure and robust voice authentication system: Feature extraction and modelling. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/60356 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51915/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Sriskandaraja, Kaavya. “Spoofing countermeasures for secure and robust voice authentication system: Feature extraction and modelling.” 2018. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/60356 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51915/SOURCE02?view=true.

MLA Handbook (7th Edition):

Sriskandaraja, Kaavya. “Spoofing countermeasures for secure and robust voice authentication system: Feature extraction and modelling.” 2018. Web. 02 Mar 2021.

Vancouver:

Sriskandaraja K. Spoofing countermeasures for secure and robust voice authentication system: Feature extraction and modelling. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/60356 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51915/SOURCE02?view=true.

Council of Science Editors:

Sriskandaraja K. Spoofing countermeasures for secure and robust voice authentication system: Feature extraction and modelling. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/60356 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:51915/SOURCE02?view=true


University of New South Wales

7. Ma, Jianbo. Modelling and compensation techniques for short duration speaker verification.

Degree: Electrical Engineering & Telecommunications, 2019, University of New South Wales

 Voice based biometric systems have been the focus of active research for a number of decades. These systems have a number of advantages including their… (more)

Subjects/Keywords: Generalized variability model; Automatic speaker verification; Short duration; Ig-vector; Duration mismatch; Twin model GPLDA

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APA (6th Edition):

Ma, J. (2019). Modelling and compensation techniques for short duration speaker verification. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/61432 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55498/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Ma, Jianbo. “Modelling and compensation techniques for short duration speaker verification.” 2019. Doctoral Dissertation, University of New South Wales. Accessed March 02, 2021. http://handle.unsw.edu.au/1959.4/61432 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55498/SOURCE02?view=true.

MLA Handbook (7th Edition):

Ma, Jianbo. “Modelling and compensation techniques for short duration speaker verification.” 2019. Web. 02 Mar 2021.

Vancouver:

Ma J. Modelling and compensation techniques for short duration speaker verification. [Internet] [Doctoral dissertation]. University of New South Wales; 2019. [cited 2021 Mar 02]. Available from: http://handle.unsw.edu.au/1959.4/61432 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55498/SOURCE02?view=true.

Council of Science Editors:

Ma J. Modelling and compensation techniques for short duration speaker verification. [Doctoral Dissertation]. University of New South Wales; 2019. Available from: http://handle.unsw.edu.au/1959.4/61432 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:55498/SOURCE02?view=true

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