Michael Wagner

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The present study elaborates on the exploitation of both linguistic and acoustic feature modeling for anger classification. In terms of acoustic modeling we generate statistics from acoustic audio descriptors, e.g. pitch, loudness, spectral characteristics. Ranking our features we see that loudness and MFCC seems most promising for all databases. For the(More)
Emotion recognition is a very active field of research. The Emotion Recognition In The Wild Challenge and Workshop (EmotiW) 2013 Grand Challenge consists of an audio-video based emotion classification challenges, which mimics real-world conditions. Traditionally, emotion recognition has been performed on laboratory controlled data. While undoubtedly(More)
The aim of this paper is to determine how vulnerable a speaker verification system is to conscious effort by impostors to mimic a client of the system. The paper explores systematically how much closer an impostor can get to another speaker’s voice by repeated attempts. Experiments on 138 speakers in the YOHO database and six people who played a role as(More)
In this paper we propose a multimodal fusion framework based on novel face-voice fusion techniques for biometric person authentication and liveness verification. Checking liveness guards the system against spoof/replay attacks by ensuring that the biometric data is captured from an authorised live person. The proposed framework based on bi-modal feature(More)
The human face is a rich source of information for the viewer and facial expressions are a major component in judging a person's affective state, intention and personality. Facial expressions are an important part of human-human interaction and have the potential to play an equally important part in human-computer interaction. This paper evaluates various(More)
Depression is a severe mental health disorder with high societal costs. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. The long-term goal of our research is to develop assistive technologies to support clinicians and sufferers in the diagnosis and monitoring of treatment(More)
Voice imitation is one of the potential threats to security systems that use automatic speaker recognition. Since prosodic features have been considered for state-of-the-art recognition systems in recent years, the question arises as to how vulnerable these features are to voice mimicking. In this study, two experiments are conducted for twelve individual(More)
In speaker veri cation, a claimed speaker's score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker's and the impostors' likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased(More)