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
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Network (past 5 years)
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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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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Disyllabic parameterisation of Vietnamese tonal F0 trajectories in likelihood ratio-based forensic voice comparison
Carne, M. & Ishihara, S., 2019, p. 780-784.Research output: Contribution to conference › Paper
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Robustness of Fundamental Frequency under Mobile Phone Codec for Estimating Likelihood Ratios in Forensic Voice Comparison
Vu, A. & Ishihara, S., 2018, p. 1pp.Research output: Contribution to conference › Paper
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Sensitivity of likelihood-ratio based forensic voice comparison under mismatched conditions of within-speaker sample sizes across databases
Ishihara, S., 2018, In: Australian Journal of Forensic Sciences. 50, 4, p. 307-322ppResearch output: Contribution to journal › Article
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Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model – Universal Background Model (GMMUBM) Approaches
Tsuge, S. & Ishihara, S., 2018, p. 17-25.Research output: Contribution to conference › Paper