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Decoding speech from intracranial recordings serves two main purposes: understanding the neural correlates of speech processing and decoding speech features for targeting speech neuroprosthetic devices. Intracranial recordings have high spatial and tempora ...
The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source domain data effectively. The i-vector based Probabilistic Linear Dis- criminant Analysis (PLDA) framework approaches thi ...
In this paper, we propose a platform based on phonological speech vocoding for examining relations between phonology and speech processing, and in broader terms, between the abstract and physical structures of speech signal. The goal of this paper is to go ...
The aim of the domain-adaptation task for speaker verification is to exploit unlabelled target domain data by using the labelled source domain data effectively. The i-vector based Probabilistic Linear Dis- criminant Analysis (PLDA) framework approaches thi ...
Phonological studies suggest that the typical subword units such as phones or phonemes used in automatic speech recognition systems can be decomposed into a set of features based on the articulators used to produce the sound. Most of the current approaches ...
Prosody plays an important role in both identification and synthesis of emotionalized speech. Prosodic features like pitch are usually estimated and altered at a segmental level based on short windows of speech (where the signal is expected to be quasi-sta ...
The prosody of the speech signal carries both linguistic and paralinguistic information. As such, there is a necessity of its modelling for the purpose of integrating it in speech technology systems. So far, there has been a multitude of proposed models fo ...
Overlapping speech has been identified as one of the main sources of errors in diarization of meeting room conversations. Therefore, overlap detection has become an important step prior to speaker diarization. Studies on conversational analysis have shown ...
Speaker diarization is the task of identifying ``who spoke when'' in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization sys ...
Speaker diarization is the task of identifying “who spoke when” in an audio stream containing multiple speakers. This is an unsupervised task as there is no a priori information about the speakers. Diagnostical studies on state-of-the-art diarization syste ...