The individual and the system : Assessing the stability of the output of a semi-automatic forensic voice comparison system

Semi-automatic systems based on traditional linguistic-phonetic features are increasingly being used for forensic voice comparison (FVC) casework. In this paper, we examine the stability of the output of a semi-automatic system, based on the long-term formant distributions (LTFDs) of F1, F2, and F3, as the channel quality of the input recordings decreases. Cross-validated, calibrated GMM-UBM log likelihood-ratios (LLRs) were computed for 97 Standard Southern British English speakers under four conditions. In each condition the same speech material was used, but the technical properties of the recordings changed (high quality studio recording, landline telephone recording, high bit-rate GSM mobile telephone recording and low bit-rate GSM mobile telephone recording). Equal error rate (EER) and the log LR cost function (Cllr) were compared across conditions. System validity was found to decrease with poorer technical quality, with the largest differences in EER (21.66%) and Cllr (0.46) found between the studio and the low bit-rate GSM conditions. However, importantly, performance for individual speakers was affected differently by channel quality. Speakers that produced stronger evidence overall were found to be more variable. Mean F3 was also found to be a predictor of LLR variability, however no effects were found based on speakers’ voice quality profiles

An investigation of supervector regression for forensic voice comparison on small data

International audienceThe present paper deals with an observer design for a nonlinear lateral vehicle model. The nonlinear model is represented by an exact Takagi-Sugeno (TS) model via the sector nonlinearity transformation. A proportional multiple integral observer (PMIO) based on the TS model is designed to estimate simultaneously the state vector and the unknown input (road curvature). The convergence conditions of the estimation error are expressed under LMI formulation using the Lyapunov theory which guaranties bounded error. Simulations are carried out and experimental results are provided to illustrate the proposed observer

The Effect Of Acoustic Variability On Automatic Speaker Recognition Systems

This thesis examines the influence of acoustic variability on automatic speaker recognition systems (ASRs) with three aims. i. To measure ASR performance under 5 commonly encountered acoustic conditions; ii. To contribute towards ASR system development with the provision of new research data; iii. To assess ASR suitability for forensic speaker comparison (FSC) application and investigative/pre-forensic use. The thesis begins with a literature review and explanation of relevant technical terms. Five categories of research experiments then examine ASR performance, reflective of conditions influencing speech quantity (inhibitors) and speech quality (contaminants), acknowledging quality often influences quantity. Experiments pertain to: net speech duration, signal to noise ratio (SNR), reverberation, frequency bandwidth and transcoding (codecs). The ASR system is placed under scrutiny with examination of settings and optimum conditions (e.g. matched/unmatched test audio and speaker models). Output is examined in relation to baseline performance and metrics assist in informing if ASRs should be applied to suboptimal audio recordings. Results indicate that modern ASRs are relatively resilient to low and moderate levels of the acoustic contaminants and inhibitors examined, whilst remaining sensitive to higher levels. The thesis provides discussion on issues such as the complexity and fragility of the speech signal path, speaker variability, difficulty in measuring conditions and mitigation (thresholds and settings). The application of ASRs to casework is discussed with recommendations, acknowledging the different modes of operation (e.g. investigative usage) and current UK limitations regarding presenting ASR output as evidence in criminal trials. In summary, and in the context of acoustic variability, the thesis recommends that ASRs could be applied to pre-forensic cases, accepting extraneous issues endure which require governance such as validation of method (ASR standardisation) and population data selection. However, ASRs remain unsuitable for broad forensic application with many acoustic conditions causing irrecoverable speech data loss contributing to high error rates

Empirical test of the performance of an acoustic-phonetic approach to forensic voice comparison under conditions similar to those of a real case

In a 2012 case in New South Wales, Australia, the identity of a speaker on several audio recordings was in question. Forensic voice comparison testimony was presented based on an auditory-acoustic-phonetic-spectrographic analysis. No empirical demonstration of the validity and reliability of the analytical methodology was presented. Unlike the admissibility standards in some other jurisdictions (e.g., US Federal Rule of Evidence 702 and the Daubert criteria, or England & Wales Criminal Practice Directions 19A), Australia's Unified Evidence Acts do not require demonstration of the validity and reliability of analytical methods and their implementation before testimony based upon them is presented in court. The present paper reports on empirical tests of the performance of an acoustic-phonetic-statistical forensic voice comparison system which exploited the same features as were the focus of the auditory-acoustic-phonetic-spectrographic analysis in the case, i.e., second-formant (F2) trajectories in /o/ tokens and mean fundamental frequency (f0). The tests were conducted under conditions similar to those in the case. The performance of the acoustic-phonetic-statistical system was very poor compared to that of an automatic system. © 2017 Elsevier B.V

Formant dynamics and durations of um improve the performance of automatic speaker recognition systems

We assess the potential improvement in the performance of MFCC-based automatic speaker recognition (ASR) systems with the inclusion of linguistic-phonetic information. Likelihood ratios were computed using MFCCs and the formant trajectories and durations of the hesitation marker um, extracted from recordings of male standard southern British English speakers. Testing was run over 20 replications using randomised sets of speakers. System validity (EER and Cllr) was found to improve with the inclusion of um relative to the baseline ASR across all 20 replications. These results offer support for the growing integration of automatic and linguistic-phonetic methods in forensic voice comparison

Reconocimiento automático de locutor e idioma mediante caracterización acústica de unidades lingüísticas

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones . Fecha de lectura: 30-06-201

A Hybrid Parameterization Technique for Speaker Identification

Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. The separation methodology is based in Joint Process Estimation under the un-correlation hypothesis between vocal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency domains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speakers are shown comparing different parameterization strategies. The results confirm the better performance of decoupled parameterization compared against approaches based on plain speech parameterization