Cantonese forensic voice comparison with higher-level features: likelihood ratio-based validation using F-pattern and tonal F0 trajectories over a disyllabic hexaphone

Rose, Philip; Wang, Bruce Xiao

doi:10.21437/odyssey.2016-47

Cited by 7 publications

(7 citation statements)

References 29 publications

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“…Further, Morrison (2009) explored the speaker-discriminatory power using dynamic vowel formant data. Meanwhile, some other studies examined the speaker-discriminatory power using suprasegmental features, e.g., long-term F0 distribution (Kinoshita et al, 2009), lexical tones (Rose & Wang, 2016), speech tempo (Lennon et al, 2019) and voice quality . Apart from testing different linguistic-phonetic features, many other studies have investigated the effect of non-linguistic factors on LR-based FVC systems, e.g., sample size (Hughes, 2017;Ishihara & Kinoshita, 2008), statistical models (Kinoshita & Wagner, 2014;Morrison, 2011a), calibration methods (Morrison & Poh, 2018), sampling variability (Ali et al, 2015), channel mismatch , reference population mismatch (Watt et al, 2020).…”

Section: The Lr Approach In Fvcmentioning

confidence: 99%

“…Several other studies have been carried out investigating the speaker-discriminatory power of vowel trajectories using Cantonese (Chen & Rose, 2012;Li & Rose, 2012;Pang & Rose, 2012;Rose & Wang, 2016).…”

Section: System Validitymentioning

confidence: 99%

“…Further, the speaker-discriminatory power of other suprasegmental variables have also been studied, e.g., Lennon et al (2019) on speech tempo, Rose & Wang (2016) on tonal F0, and on voice quality.…”

Section: System Validitymentioning

confidence: 99%

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The effect of sampling variability on overall performance and individual speakers’ behaviour in likelihood ratio-based forensic voice comparison

Wang

2022

The International Journal of Speech, Language and the Law

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In the past years, there is increasing awareness and acceptance among forensic speech scientists of using Bayesian reasoning and likelihood ratio (LR) framework for forensic voice comparison (FVC) and expressing expert conclusions. Numerous studies have explored overall performance using numerical LRs. Given that the data used for validation is a sample coming from an unknown distribution, little attention has been paid to the effect of sampling variability or individuals' behaviour. This thesis investigates these issues using linguistic-phonetic variables. First, it investigates how different configurations of training, test and reference speakers affect overall performance. The results show that variability in overall performance is mostly caused by varying the test speakers, while less variability is caused by sampling variability in the reference and training speakers. Second, this thesis explores the effect of sampling variability on overall performance and individuals' behaviour in relation to the use of linguistic-phonetic features. Results show that sampling variability affects overall performance to different extents using different features, while combining more features does not always improve overall performance. Sampling variability has limited effects on individuals in same-speaker comparisons, and most speakers are less affected by sampling variability in different-speaker comparisons when four or more features are used. Third, this thesis explores the effect of sampling variability on overall performance in relation to score distributions. Results reveal that system validity and reliability are more affected by differentspeaker score skewness, and less affected by same-speaker score skewness. Using different calibration methods reduces the effect of sampling variability to different extents. The resultsin this thesis have implications for both FVC using numerical LRs and FVC in general, as experts need to make pragmatic decisions whether numerical LR is used or not, and every decision made has implication to final evaluation results. Further, the results on score skewness and different calibration methods have potential contribution for improving FVC performance using automatic systems.

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Section: The Lr Approach In Fvcmentioning

confidence: 99%

Section: System Validitymentioning

confidence: 99%

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The effect of sampling variability on overall performance and individual speakers’ behaviour in likelihood ratio-based forensic voice comparison

Wang

2022

The International Journal of Speech, Language and the Law

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“…Many previous studies have explored the performance of linguistic features such as individual vowels and phonetic sequences using LR-based testing (e.g. Morrison 2009;Zhang et al 2011;Rose and Wang 2016). Typically, a group of speakers (often 60) is selected, split equally into training, test and reference speakers (e.g.…”

Section: Takedownmentioning

confidence: 99%

The effect of speaker sampling in likelihood ratio based forensic voice comparison

Wang

Hughes

Foulkes

2019

The International Journal of Speech, Language and the Law

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Within the field of forensic voice comparison (FVC), there is growing pressure for experts to demonstrate the validity and reliability of the conclusions they reach in casework. One benefit of a fully data-driven approach that utilises databases of speakers to compute numerical likelihood ratios (LRs) is that it is possible to estimate validity and reliability empirically. However, little is known about the stability of LR output as a function of the specific speakers sampled for use in the training, test, and reference data sets. The present study addresses this issue using two large sets of formant data: Cantonese sentence final particle /a/ and British English filled pauses UM. Experiments were replicated 100 times varying the (1) training, test and reference speakers, (2) training speakers only, (3) test speakers only, and (4) reference speakers only. The results show that varying the speakers in all three sets has the greatest effect on system stability for both the Cantonese and English variables, with the Cllr varying from 0.60 to 0.97 for /a/ and 0.32 to 1.33 for UM. However, this variability is primarily due to the effects of uncertainty in the test set. Varying only the training speaker speakers has the least effect on system stability for /a/ (Cllr range: 0.76 to 0.88), while varying reference speakers has the smallest effect for UM (Cllr range: 0.40 to 0.54). The results indicate that in LR-based FVC it is important to assess the stability of system as a function of the samples of speakers used (Cllr range) rather than just reporting a single Cllr value based on one configuration of speakers in each set. The study contributes to the general debate on reporting uncertainty in LR computation.

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“…Later in 2011, Zhang et al has introduced a fourth order DCTs on F2 and F3 which gives best performance results for a forensic voice comparison system which is based on the tokens of Standard Chinese triphthong /iau/. Afterwards, F pattern variations for adjacent sound patterns have been used (Rose & Wang, 2016;Emerich, 2012;Man, 2007;Fant, 2006). But it can be concluded by considering that transition period of gliding segments are not helpful for individual vowel identity but provide crucial information for the identification of a gliding phoneme (Weirich, 2011).…”

Section: Triphthongsmentioning

confidence: 99%

Identification of Triphthongs in Pakistani English

Farooq¹,

Mahmood²

2017

IJEL

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The present research deals with the identification of triphthongs in Pakistani English (PakE). A triphthong is the combination of three different vowels. There are five triphthongs in English (Roach, 2009) but those are absent in PakE due to the language variation phenomenon. As, we know that every language has a different lingual approach than the other therefore, if we find any similarity that is just a matter of chance and coincidence nothing else. So, in the current research, it is proposed that the native language, Urdu would affect standard pronunciation or RP in Pakistan. For the confirmation of this hypothesis, two experimental approaches are selected for the identification of triphthongs and their acoustic behavior in PakE. Therefore, sixty Pakistani speakers have been selected as a population of the research. Afterwards their speech has been recorded and analyzed in anechoic chamber. In the first step, the auditory approach has selected which reported varied vocalic segments by using syllable count method. In the second step, for knowing the acoustic behavior, the identified segments have been further investigated in PRAAT software. Then, data analysis and results have confirmed that Urdu influences and transforms the acoustic features of PakE. It is also confirmed that PakE has two triphthongs; (i) /ʊae/ (at word final position) and (ii) /ʊaɪ/ (at word medial position) which are not the part of RP phonetic inventory.

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Cantonese forensic voice comparison with higher-level features: likelihood ratio-based validation using F-pattern and tonal F0 trajectories over a disyllabic hexaphone

Cited by 7 publications

References 29 publications

The effect of sampling variability on overall performance and individual speakers’ behaviour in likelihood ratio-based forensic voice comparison

The effect of sampling variability on overall performance and individual speakers’ behaviour in likelihood ratio-based forensic voice comparison

The effect of speaker sampling in likelihood ratio based forensic voice comparison

Identification of Triphthongs in Pakistani English

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