@mastersthesis {2025,
	title = {Analysis and Synthesis of Audio with AI: from Neurological Disease to Accented Speech and Music},
	year = {2025},
	school = {Singapore University of Technology of Design},
	type = {PhD},
	address = {Singapore},
	abstract = {In the modern era, new technology is opening opportunities to help various groups of people around the world. In this thesis, deep learning and audio processing is utilized to target the needs of and develop specific applications for patients with progressive neurological diseases, speakers of non-native English accents, and amateur and leisure musicians and music enjoyers. Throughout the thesis, we deal with datasets of limited size, controllability of deep generative audio models, and creation of new datasets to target both these aspects.

First, we propose a pipeline for an automated assessment of oral diadochokinesis in neurological patients and analyze acoustic features among disease type, dysarthria type, and dysarthria severity. The results confirm some of the hypotheses about the manifestation of different dysarthria and disease types in speech while showing the major effect of dysarthria severity on oral diadochokinesis performance.

Following the investigation of dysarthric speech, we focus on another part of "non-standard" speech -- foreign accents of English. Specifically, in Text-to-Speech models, we deal with converting a speaker{\textquoteright}s accent into a different target accent while preserving their original speaker identity. We pioneer the development of accent-converting Text-to-Speech with a family of models that aim to achieve full control over accent and speaker identity in synthesized speech by disentangling the two attributes. This application could benefit minorities with non-native English accents by allowing them to customize the system{\textquoteright}s speech output for better intelligibility.

Immersed in controllable generative models, we continue our journey in the music generation domain with focus on high controllability. To counter the odds of limited availability and size of public datasets and to enable fine controllability of the proposed models, we propose and demonstrate methods of enhancing and augmenting music datasets, with the introduction of MidiCaps, a large-scale captioned MIDI dataset, and with MusicBench, a music audio dataset with enhanced text captions. We utilize MusicBench to build Mustango, a controllable Text-to-Music generation system with focus on music specific commands of chords, beats, key, and tempo. Finally, we introduce SonicMaster, a text-controllable all-in-one music restoration and mastering model that we train with our proposed SonicMaster dataset.
},
	author = {J. Melechovsky}
}
@conference {2024,
	title = {Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {With rapid globalization, the need to build inclusive and representative speech technology cannot be overstated. Accent is an important aspect of speech that needs to be taken into consideration while building inclusive speech synthesizers. Inclusive speech technology aims to erase any biases towards specific groups, such as people of certain accent. We note that state-of-the-art Text-to-Speech (TTS) systems may currently not be suitable for all people, regardless of their background, as they are designed to generate high-quality voices without focusing on accent. In this paper, we propose a TTS model that utilizes a Multi-Level Variational Autoencoder with adversarial learning to address accented speech synthesis and conversion in TTS, with a vision for more inclusive systems in the future. We evaluate the performance through both objective metrics and subjective listening tests. The results show an improvement in accent conversion ability compared to the baseline.},
	url = {https://arxiv.org/abs/2406.01018},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
@conference {2022,
	title = {Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {Accent plays a significant role in speech communication, influencing understanding capabilities and also conveying a person{\textquoteright}s identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker{\textquoteright}s speech that is converted to any desired target accent. Our thorough experiments validate the effectiveness of our proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the ability to manipulate accents in the synthesized speech and provide a promising avenue for future accented TTS research.},
	url = {https://arxiv.org/abs/2211.03316},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
