Speaker Identification for Swiss German with Spectral and Rhythm Features

We present results of speech rhythm analysis for automatic speaker identification. We expand previous experiments using similar methods for language identification. Features describing the rhythmic properties of salient changes in signal components are extracted and used in an speaker identification task to determine to which extent they are descriptive of speaker variability. We also test the performance of state-of-the-art but simple-to-extract frame-based features. The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines. Results suggest that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity

Temporal Parameters of Spontaneous Speech in Forensic Speaker Identification in Case of Language Mismatch: Serbian as L1 and English as L2

Celem badania jest analiza możliwości identyfikacji mówcy kryminalistycznego i sądowego podczas zadawania pytań w różnych językach, z wykorzystaniem parametrów temporalnych. (wskaźnik artykulcji, wskaźnik mowy, stopień niezdecydowania, odsetek pauz, średnia czas trwania pauzy). Korpus obejmuje 10 mówców kobiet z Serbii, które znają język angielksi na poziomie zaawwansowanym. Patrametry są badane z wykorzystaniem beayesowskiego wzoru wskaźnika prawdopodobieństwa w 40 parach tcyh samych mówców i w 230 parach różnych mówców, z uwzględnieniem szacunku wskaźnika błędu, równiego wskaźnika błędu i Całościowego Wskaźnika Prawdopodobieństwa. badanie ma charakter pionierski w zakresie językoznawstwa sądowego i kryminalistycznego por1) ónawczego w parze jezyka serbskiego i angielskiego, podobnie, jak analiza parametrów temporalnych mówców bilingwalnych. Dalsze badania inny skoncentrować się na porównaniu języków z rytmem akcentowym i z rytmem sylabicznym. The purpose of the research is to examine the possibility of forensic speaker identification if question and suspect sample are in different languages using temporal parameters (articulation rate, speaking rate, degree of hesitancy, percentage of pauses, average pause duration). The corpus includes 10 female native speakers of Serbian who are proficient in English. The parameters are tested using Bayesian likelihood ratio formula in 40 same-speaker and 360 different-speaker pairs, including estimation of error rates, equal error rates and Overall Likelihood Ratio. One-way ANOVA is performed to determine whether inter-speaker variability is higher than intra- speaker variability across languages. The most successful discriminant is degree of hesitancy with ER of 42.5%/28%, (EER: 33%), followed by average pause duration with ER 35%/45.56%, (EER: 40%). Although the research features a closed-set comparison, which is not very common in forensic reality, the results are still relevant for forensic phoneticians working on criminal cases or as expert witnesses. This study pioneers in forensically comparing Serbian and English as well as in forensically testing temporal parameters on bilingual speakers. Further research should focus on comparing two stress-timed or two syllable-timed languages to test whether they will be more comparable in terms of temporal aspects of speech.

Identyfikacja parametrów czasowych mowy spontanicznej mówców kryminalistycznych w przypadku niedopasowania językowego: język serbski jako L1 i język angielski jako L2

The purpose of the research is to examine the possibility of forensic speaker identification if question and suspect sample are in different languages using temporal parameters (articulation rate, speaking rate, degree of hesitancy, percentage of pauses, average pause duration). The corpus includes 10 female native speakers of Serbian who are proficient in English. The parameters are tested using Bayesian likelihood ratio formula in 40 same-speaker and 360 different-speaker pairs, including estimation of error rates, equal error rates and Overall Likelihood Ratio. One-way ANOVA is performed to determine whether inter-speaker variability is higher than intra- speaker variability across languages. The most successful discriminant is degree of hesitancy with ER of 42.5%/28%, (EER: 33%), followed by average pause duration with ER 35%/45.56%, (EER: 40%). Although the research features a closed-set comparison, which is not very common in forensic reality, the results are still relevant for forensic phoneticians working on criminal cases or as expert witnesses. This study pioneers in forensically comparing Serbian and English as well as in forensically testing temporal parameters on bilingual speakers. Further research should focus on comparing two stress-timed or two syllable-timed languages to test whether they will be more comparable in terms of temporal aspects of speech. Celem badania jest analiza możliwości identyfikacji mówcy kryminalistycznego i sądowego podczas zadawania pytań w różnych językach, z wykorzystaniem parametrów temporalnych. (wskaźnik artykulcji, wskaźnik mowy, stopień niezdecydowania, odsetek pauz, średnia czas trwania pauzy). Korpus obejmuje 10 mówców kobiet z Serbii, które znają język angielksi na poziomie zaawwansowanym. Patrametry są badane z wykorzystaniem beayesowskiego wzoru wskaźnika prawdopodobieństwa w 40 parach tcyh samych mówców i w 230 parach różnych mówców, z uwzględnieniem szacunku wskaźnika błędu, równiego wskaźnika błędu i Całościowego Wskaźnika Prawdopodobieństwa. badanie ma charakter pionierski w zakresie językoznawstwa sądowego i kryminalistycznego por1) ónawczego w parze jezyka serbskiego i angielskiego, podobnie, jak analiza parametrów temporalnych mówców bilingwalnych. Dalsze badania inny skoncentrować się na porównaniu języków z rytmem akcentowym i z rytmem sylabicznym.

CIVIL Corpus: Voice Quality for Speaker Forensic Comparison

AbstractThe most frequent way in which criminals disguise their voices implies changes in phonation types, but it is difficult to maintain them for a long time. This mechanism severely hampers identification. Currently, the CIVIL corpus comprises 60 Spanish speakers. Each subject performs three tasks: spontaneous conversation, carrier sentences and reading, using modal, falsetto and creak(y) phonation. Two different recording sessions, one month apart, were conducted for each speaker, who was recorded with microphone, telephone and electroglottography. This is the first (open-access) corpus of disguised voices in Spanish. Its main purpose is finding biometric traces that remain in voice despite disguise

Strength of forensic voice comparison evidence from the acoustics of filled pauses

This study investigates the evidential value of filled pauses (FPs, i.e. um, uh) as variables in forensic voice comparison. FPs for 60 young male speakers of standard southern British English were analysed. The following acoustic properties were analysed: midpoint frequencies of the first three formants in the vocalic portion; ‘dynamic’ characterisations of formant trajectories (i.e. quadratic polynomial equations fitted to nine measurement points over the entire vowel); vowel duration; and nasal duration for um. Likelihood ratio (LR) scores were computed using the Multivariate Kernel Density formula (MVKD; Aitken and Lucy, 2004) and converted to calibrated log10 LRs (LLRs) using logistic-regression (Brümmer et al., 2007). System validity was assessed using both equal error rate (EER) and the log LR cost function (Cllr; Brümmer and du Preez, 2006). The system with the best performance combines dynamic measurements of all three formants with vowel and nasal duration for um, achieving an EER of 4.08% and Cllr of 0.12. In terms of general patterns, um consistently outperformed uh. For um, the formant dynamic systems generated better validity than those based on midpoints, presumably reflecting the additional degree of formant movement in um caused by the transition from vowel to nasal. By contrast, midpoints outperformed dynamics for the more monophthongal uh. Further, the addition of duration (vowel or vowel and nasal) consistently improved system performance. The study supports the view that FPs have excellent potential as variables in forensic voice comparison cases

Strength of forensic voice comparison evidence from the acoustics of filled pauses

This study investigates the evidential value of filled pauses (FPs, i.e. um, uh) as variables in forensic voice comparison. FPs for 60 young male speakers of standard southern British English were analysed. The following acoustic properties were analysed: midpoint frequencies of the first three formants in the vocalic portion; ‘dynamic’ characterisations of formant trajectories (i.e. quadratic polynomial equations fitted to nine measurement points over the entire vowel); vowel duration; and nasal duration for um. Likelihood ratio (LR) scores were computed using the Multivariate Kernel Density formula (MVKD; Aitken and Lucy, 2004) and converted to calibrated log10 LRs (LLRs) using logistic-regression (Brümmer et al., 2007). System validity was assessed using both equal error rate (EER) and the log LR cost function (Cllr; Brümmer and du Preez, 2006). The system with the best performance combines dynamic measurements of all three formants with vowel and nasal duration for um, achieving an EER of 4.08% and Cllr of 0.12. In terms of general patterns, um consistently outperformed uh. For um, the formant dynamic systems generated better validity than those based on midpoints, presumably reflecting the additional degree of formant movement in um caused by the transition from vowel to nasal. By contrast, midpoints outperformed dynamics for the more monophthongal uh. Further, the addition of duration (vowel or vowel and nasal) consistently improved system performance. The study supports the view that FPs have excellent potential as variables in forensic voice comparison cases

Evaluating the forensic importance of glottal source features through the voice analysis of twins and non-twin siblings

In this study we have analyzed 853 tokens of the vowel filler [ei], extracted from spontaneous speech fragments of 54 male Spanish speakers (NorthCentral Peninsular variety), each one recorded on two separate sessions. The speakers — to be compared in a pairwise fashion - were divided in four groups: 24 monozygotic (MZ) twins, 10 dizygotic (DZ) twins, 8 non-twin brothers and 12 unrelated speakers. From the extracted vowel fillers, considered long enough for a glottal analysis (around 160 milliseconds), a vector of 68 glottal parameters was created. Our hypothesis that higher similarity values would be found in the intra-pair comparison ofMZ twins than in DZ twins, brothers or unrelated speakers was confirmed, which suggests that the glottal parameters under investigation are genetically influenced. This finding seems of great forensic importance, as a phonetic parameter is considered forensically robust provided that it exhibits large between-speaker variation while it remains as consistent as possible for each speaker (i.e. small within-speaker variation)

Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification

In this paper a novel cross-device text-independent speaker verification architecture is proposed. Majority of the state-of-the-art deep architectures that are used for speaker verification tasks consider Mel-frequency cepstral coefficients. In contrast, our proposed Siamese convolutional neural network architecture uses Mel-frequency spectrogram coefficients to benefit from the dependency of the adjacent spectro-temporal features. Moreover, although spectro-temporal features have proved to be highly reliable in speaker verification models, they only represent some aspects of short-term acoustic level traits of the speaker's voice. However, the human voice consists of several linguistic levels such as acoustic, lexicon, prosody, and phonetics, that can be utilized in speaker verification models. To compensate for these inherited shortcomings in spectro-temporal features, we propose to enhance the proposed Siamese convolutional neural network architecture by deploying a multilayer perceptron network to incorporate the prosodic, jitter, and shimmer features. The proposed end-to-end verification architecture performs feature extraction and verification simultaneously. This proposed architecture displays significant improvement over classical signal processing approaches and deep algorithms for forensic cross-device speaker verification.Comment: Accepted in 9th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS 2018