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

Automatic phonetic-unit selection and modelling techniques for forensic voice comparison

Acoustic phonetic approaches to Forensic Voice Comparison (FVC) have traditionally involved a labor-intensive process of identifying speaker-discriminative phonetic units in speech for speaker characterization in forensic court cases. Automatic FVC systems have employed Gaussian Mixture Model Universal Background Model (GMM-UBM) modelling without explicitly accounting for phonetic unit selection, and with little research into the supervector-based techniques prevalent in recent speaker recognition studies. The goal of this thesis is therefore to improve the efficiency and performance of FVC systems by investigating automatic techniques for detecting speaker-discriminative speech segments, and new modelling techniques that complement conventional FVC systems.A study of Hidden-Markov-Model (HMM) based automatic phonetic segmentation on GMM-UBM FVC systems demonstrates that nasals and vowels contribute the most in system improvements. An investigation of phone recognizer endpoint accuracy demonstrates a trade-off between validity and reliability as a function of the duration of recognized tokens. A novel hybrid HMM/GMM-based automatic phonetic selector was proposed with better phonetic-unit detection accuracy (reduced miss rate) than a conventional HMM-based automatic selector. Substantial FVC improvement was observed from fusion of a system based on manually selected tokens with a baseline system based on all speech-active segments, across various database conditions with approximately 50% human effort reduction in manual token selection, by incorporating the automatic phonetic selector designed with near zero miss rate.A novel adaptation and fusion strategy, termed Separate MAP (SMAP) adaptation, was proposed for GMM-UBM modelling that yielded substantial FVC improvements and was more robust under limited data conditions in comparison with conventional mean-only or full MAP adaptation. The strategy involves fusing multiple MAP adapted sub-configurations wherein smaller subsets of GMM parameters are MAP adapted separately, potentially allowing more accurate parameter estimation and alleviating the overfitting issue, based on the same short speaker-specific utterances.A first study into supervector regression methods in automatic FVC systems was conducted across numerous database conditions, and substantial improvements from supervector regression configurations relative to GMM-UBM and Support Vector Machine (SVM) baseline systems were observed. Supervector regression was evaluated as extremely effective in same-speaker comparisons, producing greater strength of evidence if the suspect on trial is indeed the offender