Automatic Speaker Recognition (ASR) involves a complex range of processes to extract, model, and compare speaker-specific information from a pair of voice samples.Using heavily controlled recordings, this paper explores the impact of specific vocal conditions (i.e.vocal setting, disguise, accent guises) on ASR performance.When vocal conditions are matched, ASR performance is generally excellent (whisper is an exception).When conditions are mismatched, as in most forensic cases, we see an increase in discrimination and calibration error in some cases.The most problematic mismatches are those involving whisper and supralaryngeal vocal settings; these produce the greatest phonetic changes to speech.Mismatches involving high pitch also produce poor performance, although this appears to be driven by speaker-specific differences in articulatory implementation.We discuss the implications of the findings for the use of ASR in forensic casework and the interpretability of system output.

Variations in vocal effort can create challenges for speaker recognition systems that are optimized for use with neutral speech. The Lombard effect and whisper are two commonly-occurring forms of vocal effort variation that result in non-neutral speech, the first due to noise exposure and the second due to intentional adjustment on the part of the speaker. In this article, a comparative evaluation of speaker recognition performance in non-neutral conditions is presented using multiple Lombard effect and whisper corpora. The detrimental impact of these vocal effort variations on discrimination and calibration performance on global, per-corpus, and per-speaker levels is explored using conventional error metrics, along with visual representations of the model and score spaces. A non-neutral speech detector is subsequently introduced and used to inform score calibration in several ways. Two calibration approaches are proposed and shown to reduce error to the same level as an optimal calibration approach that relies on ground-truth vocal effort information. This article contributes a generalizable methodology towards detecting vocal effort variation and using this knowledge to inform and advance speaker recognition system behavior.

The present paper describes a statistical modeling procedure that was developed to account for the fact that, in a forensic voice comparison analysis conducted for a particular case, there was a long time interval between when the questioned- and known-speaker recordings were made (six years), but in the sample of the relevant population used for training and testing the forensic voice comparison system there was a short interval (hours to days) between when each of multiple recordings of each speaker was made. The present paper also includes results of empirical validation of the procedure. Although based on a particular case, the procedure has potential for wider application given that relatively long time intervals between the recording of questioned and known speakers are not uncommon in casework.

Whisper is a commonly encountered form of speech that differs significantly from modal speech. As speaker recognition technology becomes more ubiquitous, it is important to assess the abilities and limitations of systems in the presence of variability such as whisper. In this paper, a comparative evaluation of whispered speaker recognition performance across two independent datasets is presented. Whisper-neutral speech comparisons are observed to consistently degrade performance relative to both neutral-neutral and whisper-whisper comparisons. An i-vector-based approach to whisper detection is introduced, and is shown to perform accurately across datasets even at short durations. The output of the whisper detector is subsequently used to select score calibration parameters for whispered speech comparisons, leading to a reduction in global calibration and discrimination error.

This study proposes a novel deep neural network (DNN) based approach to language identification (LID) for the NIST 2015 Language Recognition (LRE) i-Vector Machine Learning Challenge. State-of-the-art DNN based LID systems utilize large amounts of labeled training data. The 2015 LRE i-Vector Machine Learning Challenge limits the access to only ready-to-use i-Vectors for LID system training and testing. This poses unique challenges in designing DNN based LID systems, since optimized front-ends and network architectures can no longer be used. We propose to use the training i-Vectors to train an initial DNN for LID. Next, we present a novel strategy to use this initial DNN to estimate out-of-set language labels from the development data. The final DNN for LID is trained using the original training data, and the estimated out-of-set language data. We show that augmenting the training set with out-of-set labels leads to significant improvement in the LID performance. Our approach obtains very competitive costs (defined by NIST) of 26.56, and 25.98 respectively, on the progress and evaluation subsets of the challenge. Since the amount of training data is very limited (300 i-Vectors per language), this study outlines a successful recipe for DNN based LID using very limited resources.

In this paper, we present the system developed by the Center for Robust Speech Systems (CRSS), University of Texas at Dallas, for the NIST 2015 language recognition i-vector machine learning challenge. Our system includes several subsystems, based on Linear Discriminant Analysis - Support Vector Machine (LDA-SVM) and deep neural network (DNN) approaches. An important feature of this challenge is the emphasis on out-of-set language detection. As a result, our system development focuses mainly on the evaluation and comparison of two different out-of-set language detection strategies: direct out-of-set detection and indirect out-of-set detection. These out-of-set detection strategies differ mainly on whether the unlabeled development data are used or not. The experimental results indicate that indirect out-of-set detection strategies used in our system could efficiently exploit the unlabeled development data, and therefore consistently outperform the direct out-of-set detection approach. Finally, by fusing four variants of indirect out-of-set detection based subsystems, our system achieves a relative performance gain of up to 45%, compared to the baseline cosine distance scoring (CDS) system provided by organizer.

Sustaining automatic speaker verification(ASV) systems from spoofing attacks remains an essential challenge, even if significant progress in ASV has been achieved in recent years. In this study, an automatic spoofing detection approach using an i-vector framework is proposed. Two approaches are used for frame-level feature extraction: cepstral-based Perceptual Minimum Variance Distortionless Response (PMVDR), and non-linear speech-production-motivated Teager Energy Operator (TEO) Critical Band (CB) Autocorrelation Envelope (Auto-Env). An utterance-level i-vector for each recording is formed by concatenating PMVDR and TEO-CB-Auto-Envi-vectors, followed by linear discriminative analysis (LDA) for maximizing the ratio of between-class to within-class scatterings. A Gaussian classifier and DNN are also investigated for back-end scoring. Experiments using the ASVspoof 2015 corpus show that our proposed method successfully detects spoofing attacks. By combining the TEO-CB-Auto-Env and PMVDR features, a relative 76.7% improvement in terms of EER is obtained compared with the best single-feature system.

The Lombard effect is the involuntary tendency of speakers to increase their vocal effort in noisy environments in order to maintain intelligible communication. This study assesses the impact of the Lombard effect on the performance of a current speaker verification system. Lombard speech produced in the presence of several noise types and noise levels is drawn from the UT-Scope corpus. The performance of an i-vector PLDA (Probabilistic Linear Discriminant Analysis) system is observed to degrade significantly with Lombard speech. The resulting error rates are found to be dependent on the noise type and noise level. A score calibration scheme based on Quality Measure Functions (QMFs) is adopted, allowing noise information to be incorporated into calibration. This approach leads to a reduction in discrimination error relative to conventional calibration.

The gradual changes that occur in the human voice due to aging create challenges for speaker verification. This study presents an approach to calibrating the output scores of a speaker verification system using the time interval between comparison samples as additional information. Several functions are proposed for the incorporation of this time information, which is viewed as aging information, in a conventional linear score calibration transformation. Experiments are presented on data with shortterm aging intervals ranging between 2 months and 3 years, and long-term aging intervals of up to 30 years. The aging calibration proposal is shown to offset the decreased discrimination and calibration performance for both shortand long-term intervals, and to extrapolate well to unseen aging intervals. Relative reductions in Cuℓℓr(cost of log-likelihood ratio) of 1-4% and 10-43% are obtained at shortand long-term intervals, respectively. Assuming that a system has knowledge of the time interval between samples under comparison, this approach represents a straightforward means of compensating for the detrimental impact of aging on speaker verification performance.

Being able to communicate efficiently has been acknowledged as a vital skill in many different domains.In particular, team communication skills are of key importance in the operation of complex machinery such as aircrafts, maritime vessels and such other, highly-specialized, civilian or military vehicles, as well as the performance of complex tasks in the medical domain.In this paper, we propose to use prosodic accommodation and turntaking organisation to provide objective metrics of communication skills.To do this, human-factors evaluations, via a coordination Demand Analysis (CDA), were used in conjunction with a dynamic model of prosodic accommodation and turn-taking organisation.Using conversational speech from airline pilots involved in a collaborative task (decision-making exercise), our study reveals that interpersonal coordination mechanisms are indicative of human evaluation of pilots' communication skills.We discuss our results in terms of relevance for training simulation for personnel in safety or mission critical environments.

Assessing the impact of ageing on biometric systems is an important challenge. In this paper, an i-vector speaker verifi-cation framework is used to evaluate the impact of long-term ageing on state-of-the-art speaker verification. Using the Trin-ity College Dublin Speaker Ageing (TCDSA) database, it is ob-served that the performance of the i-vector system, in terms of both discrimination and calibration, degrades progressively as the absolute age difference between training and testing sam-ples increases. In the case of male speakers, the equal error rate (EER) increases from 4.61 % at an ageing difference of 0–1 years to 32.74 % at an age difference of 51–60 years. The performance of a Gaussian Mixture Model- Universal Back-ground Model (GMM-UBM) system is presented for compari-son. It is shown that while the i-vector system outperforms the GMM-UBM system, as absolute age difference increases, the performance of both degrades at a similar rate. It is concluded that long-term ageing variability is distinct from everyday inter-session variability, and therefore must be dealt with via dedi-cated compensation strategies.

Dealing with the effect of vocal ageing on speaker verification is an important challenge. In this paper, a new approach to improving speaker verification performance in the presence of long-term ageing is presented. Analogous to eigenchannel compensation, the proposed eigenageing compensation method operates by adapting a speaker model to a test sample based on a predetermined ageing subspace. An experimental evaluation of the new method, using the Trinity College Dublin Speaker Ageing database, demonstrates it to be very effective at reducing long-term speaker verification error rates, and shows it to compare favourably with our previous stacked classifier technique. Index Terms: speaker verification, ageing, eigenanalysis 1.

The comparison of non-contemporary speech samples is common in forensic speaker recognition cases. It has yet to be established however, to what extent the time interval between non-contemporary samples can increase before a problem is created for forensic automatic speaker recognition. This paper presents results of a human listener test designed to evaluate the detectability of vocal ageing over increasing intervals of up to 30 years. Subsequently, a forensic automatic speaker recognition evaluation of 15 ageing males at increasing intervals of up to 60 years is presented. It is shown that at intervals of around 10 years, the average detectability of vocal ageing by humans is just above chance. As the interval rises to 30 years, vocal ageing is detected 90% of the time. In the automatic system, vocal ageing is manifested as a drop in intra-speaker likelihood ratios (LRs) as the time interval between non-contemporary samples increases. At an interval of 30 years, LRs for the vast majority of intra-speaker comparisons fall below a value of 100 commonly interpreted as ‘moderate support’ on a verbal LR scale. Our findings indicate that at a time-lapse of 30 years, vocal ageing creates significant problems for forensic automatic speaker recognition.

Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) divergence is often used. However, since there is no closed form expression to compute it, it can only be approximated. We propose lower and upper bounds for the KL divergence, which lead to a new approximation and interesting insights into previously proposed approximations. An application to the comparison of speaker models also shows how such approximations can be used to validate assumptions on the models.

Performing speaker verification in the simultaneous presence of ageing progression and changing speech sample quality is an important, open problem. The issues of ageing and quality variation go hand in hand; the effect of ageing increases with time, while variations in quality are also more likely to be encountered as time passes. In this work we demonstrate the effect of ageing on speaker verification performance, and show the relationship between quality variation and verification score via a range of established quality measures. We employ a stacked classifier framework to combine the output of the baseline verification system with ageing information and quality measures. This new approach to long-term speaker verification allows for a multi-dimensional decision boundary that significantly improves upon the baseline performance. The proposed framework is evaluated on the Trinity College Dublin Speaker Ageing Database. Index Terms: speaker verification, ageing, stacked classifier

Auditory based front-ends for speech recognition have been compared before, but this paper focuses on two of the most promising algorithms for noise robustness in automatic speech recognition (ASR). The feature sets are Zero-Crossings with Peak Amplitudes (ZCPA) and the recently introduced Power-Law Nonlinearity and Power-Bias Subtraction (PNCC). Standard Mel-Frequency Cepstral Coefficients (MFCC) are also tested for reference. The performance of all features is reported on the TIMIT database using a HMM-based recogniser. It is found that the PNCC features outperform MFCC in clean conditions and are most robust to noise. ZCPA performance is shown to vary widely with filter bank configuration and frame length. The ZCPA performance is poor in clean conditions but is the least affected by white noise. PNCC is shown to be the most promising new feature set for robust ASR in recent years.