Spoofing detection algorithms in voice biometrics are adversely affected by differences in the speech characteristics of the various target users. In this paper, we propose a novel speaker normalisation technique that employs adversarial multi-task learning to compensate for this speaker variability. The proposed system is designed to learn a feature space that discriminates between genuine and replayed speech while simultaneously reduces the discrimination between different speakers. We initially characterise the impact of speaker variability and quantify the effect of the proposed speaker normalisation technique directly on the feature distributions. Following this, we validate the technique on spoofing detection experiments carried out on two different corpora, ASVSpoof 2017 v2.0 and BTAS 2016 replay, and demonstrate its effectiveness. We obtain EER of 7.11% and 0.83% on the two corpora respectively, lower than that of all relevant baselines.

Replay attack refers to the use of recorded speech in an attempt to spoof an automatic speaker verification system and the development of countermeasures that can detect these attacks is an active area of research. This paper investigates the effect of phoneme specific information on replay attack detection. It then develops a replay detection system that employs phoneme specific genuine and spoof models and compares novel scoring methods that take into account phonetic information obtained from a suitable phoneme recogniser. Experiment result on the ASVSpoof 2017 V2.0 corpus indicated that replayed speech may be easier to detect from speech corresponding to some phonemes compared to others and consequently judicious use of phoneme specific models can improve replay detection systems.

The development of automatic systems that can detect replayed speech has emerged as a significant research challenge for securing voice biometric systems and is the focus of this paper. Specifically, this paper proposes two novel features to capture the static and dynamic characteristics of the signal from the modulation spectrum, which complement short term spectral features for use in replay detection. The modulation spectral centroid frequency feature is proposed as a vector representation of the first order spectral moments of the modulation spectrum. In conjunction to this, the long term spectral average serves to capture the static characteristics of the modulation spectrum. The proposed system, employing a GMM back-end, was evaluated on the ASVSpoof 2017 dataset and found to yield an EER of 6.54%.

Spoofing detection systems for automatic speaker verification have moved from only modelling voiced frames to modelling all speech frames. Unvoiced speech has been shown to carry information about spoofing attacks and anti-spoofing systems may further benefit by treating voiced and unvoiced speech differently. In this paper, we separate speech into low and high energy frames and independently model the distributions of both to form two spoofing detection systems that are then fused at the score level. Experiments conducted on the ASVspoof 2015, BTAS 2016 and Spoofing and Anti-Spoofing (SAS) corpora demonstrate that the proposed approach of fusing two independent high and low energy spoofing detection systems consistently outperforms the standard approach that does not distinguish between high and low energy frames.

Flavor is often a pleasurable sensory perception we experience daily while eating and drinking. However, the sensation of flavor is rarely considered in the age of digital communication mainly due to the unavailability of flavors as a digitally controllable media. This paper introduces a digital instrument (Digital Flavor Synthesizing device), which actuates taste (electrical and thermal stimulation) and smell sensations (controlled scent emitting) together to simulate different flavors digitally. A preliminary user experiment is conducted to study the effectiveness of this method with predefined five different flavor stimuli. Experimental results show that the users were effectively able to identify different flavors such as minty, spicy, and lemony. Moreover, we outline several challenges ahead along with future possibilities of this technology. In summary, our work demonstrates a novel controllable instrument for flavor simulation, which will be valuable in multimodal interactive systems for rendering virtual flavors digitally.

In recent years, digital media technologies expand horizons into non-traditional sensory modalities, for instance, the sense of taste. This demonstration presents `taste+,' which digitally improves the taste sensations of food and beverages without additional flavoring ingredients. It primarily utilizes weak and controlled electrical pulses on the human tongue to enhance sourness, saltiness, and bitterness of food or beverages. Taste+ consists of two prototype utensils, a bottle and a spoon. Both utensils have embedded electronic control modules to achieve enhanced taste sensations. These modules apply controlled electrical pulses on the tongue through silver electrodes attached to the mouth pieces. Furthermore, different superimposed colors symbolize distinct taste sensations, lemon green represents sour, ocean blue is salty, red for red wine bitter. The initial experimental results suggested that sourness and saltiness are the main sensations that could be evoked while bitterness has comparatively mild responses. Furthermore, we also observed several mixed sensations such as salty-sour together.