Speech Sounds and Sociophonetics
Speech sounds are fascinating! I combine methods from signal processing and Machine Learning to gain more understanding about speech processes and they information the convey linguistic and sociolinguistic. Some current publications are the following.
- Themistocleous, Charalambos (2017). Modern Greek vowels and the nature of acoustic gradience. Phonetica, 74, 157 – 172. http://www.karger.com/DOI/10.1159/000450554 |Link|
- Themistocleous, Charalambos (2016). The bursts of stops can convey dialectal information. Journal of the Acoustical Society of America EL 140(4). http://dx.doi.org/10.1121/1.4964818 |Link| |Pdf|
Prosody: Prosodic variation and change
The melody of speech, also known as prosody, marks constituents in speech as prominent, designates the boundaries of domains (such as the prosodic word and the prosodic phrase), and conveys different melodies (as in questions, statements, and commands). A number of studies have shown that the melody of speech is structured. Understanding this structure and how this structure interacts with the overall cognition and understanding of information is a central objective of my research.
- Themistocleous, Charalambos (2016). Seeking an anchorage: Evidence from the tonal alignment of the Cypriot Greek prenuclear pitch accent. Language and Speech., 59(4). 433-461, doi: 10.1177/0023830915614602. |Link|
- Themistocleous, Charalambos (2014). Edge-Tone Eﬀects and Prosodic Domain Eﬀects on Final Lengthening. Linguistic Variation 14(1). 129– 160. |Link|
- Themistocleous Charalambos (2011). Prosody and Information Structure in Greek (in Greek). PhD Thesis. University of Athens Greece.
Computational Linguistics and Natural Language Processing
The impressive achievements in areas, such as text-to-speech, speech perception, machine translation, image description generation, and semantic interpretation of the past decade were made possible by employing methods from signal processing, NLP, AI, and machine learning. Methods, such as Deep Neural Networks, SVMs, HMMs, can offer insights about human perception and cogni tion, namely how humans perceive, process, and store information from speech signals, texts, vision, etc., and also how they encode information. Within this framework, I explore the intersection between speech perception/production and language understanding.
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