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The performance of speaker recognition systems has considerably improved in the last decade. This is mainly due to the development of Gaussian mixture model-based systems and in particular to the use of i-vectors. These systems handle relatively well noise ...
Advances in Automatic Speech Recognition (ASR) over the last decade opened new areas of speech-based automation such as in Air-Traffic Control (ATC) environments. Currently, voice communication and Controller Pilot Data Link Communications are the only way ...
We propose a deep neural network based image-to-image translation for domain adaptation, which aims at finding translations between image domains. Despite recent GAN based methods showing promising results in image-to-image translation, they are prone to f ...
Vocal tract length normalisation (VTLN) is well established as a speaker adaptation technique that can work with very little adaptation data. It is also well known that VTLN can be cast as a linear transform in the cepstral domain. Building on this latter ...
State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...
Due to its convenience, biometric authentication, especial face authentication, has become increasingly mainstream and thus is now a prime target for attackers. Presentation attacks and face morphing are typical types of attack. Previous research has shown ...
Children speech recognition based on short-term spectral features is a challenging task. One of the reasons is that children speech has high fundamental frequency that is comparable to formant frequency values. Furthermore, as children grow, their vocal ap ...
In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent ( among many others) are suppress ...
Although current trends in speech processing consider deep learning through data-driven technologies, many potential applications exhibit lack of training or development data. Therefore, considerably light signal processing techniques are still of interest ...
In i-vector based speaker recognition systems, back-end classifiers are trained to factor out nuisance information and retain only the speaker identity. As a result, variabilities arising due to gender, language and accent ( among many others) are suppress ...