Research output per year
Research output per year
Personal profile
Research interests
Forensic Voice/text Comparison, Computational Linguistics, Intonational Modelling, Speech & Language Processing, Similarities/Differences between Individuals, Japanese Language, Japanese Linguistics, Computer Assisted Language Learning and Teaching
Available student projects
Researcher's projects
2015- Forensic Voice Comparison in Vietnamese (International Association for Forensic Phonetics and Acoustics Research Grant) (Main CI: Dr Yuko Kinoshita)
2013-2015 Above & Beyond Speech, Language and Music: A Virtual Lab for Human Communication Science <http://alveo.edu.au/> (Commonwealth Department of Education and Training Grant)
2012 Forensic Voice Comparison in Japanese (2012 Endeavour Reseach Grant)
2010-2011 AusTalk Project <https://austalk.edu.au/> (ARC LIEF100100211)
Qualifications
BEd (Shizuoka), Grad Dip, MA(ANU) MSc (Macquarie), PhD (ANU)
Expertise Areas
- Natural Language Processing
- Signal Processing
- Japanese Language
- Computational Linguistics
- Laboratory Phonetics and Speech Science
Fingerprint
Dive into the research topics where Shunichi Ishihara is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
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Likelihood ratio estimation for authorship text evidence: An empirical comparison of score- and feature-based methods
Ishihara, S. & Carne, M., 2022, In: Forensic Science International. 334Research output: Contribution to journal › Article
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Mahalanobis distance with an adapted within-author covariance matrix: An authorship verification experiment
Ishihara, S., 2022, In: Digital Scholarship in the Humanities. 37, 4, p. 1051–1072Research output: Contribution to journal › Article
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Score-based likelihood ratios for linguistic text evidence with a bag-of-words model
Ishihara, S., 2021, In: Forensic Science International. 327, p. 1-20Research output: Contribution to journal › Article
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Feature-based forensic text comparison using a Poisson model for likelihood ratio estimation
Carne, M. & Ishihara, S., 2020, p. 32-42.Research output: Contribution to conference › Paper
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The influence of background data size on the performance of a score-based likelihood ratio system: A case of forensic text comparison
Ishihara, S., 2020, p. 1-11.Research output: Contribution to conference › Paper