The use of intracranial recordings to decode human language: Challenges and opportunities
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In this thesis, methods and models are developed and presented aiming at the estimation, restoration and transformation of the characteristics of human speech. During a first period of the thesis, a concept was developed that allows restoring prosodic voic ...
This paper investigates the combination of cepstral normalization and cochlear implant-like speech processing for microphone array- based speech recognition. Testing speech signals are recorded by a circular microphone array and are subsequently processed ...
Progressive apraxia of Speech (PAoS) is a progressive motor speech disorder associated with neurodegenerative disease causing impairment of phonetic encoding and motor speech planning. Clinical observation and acoustic studies show that duration analysis p ...
This thesis deals with signal-based methods that predict how listeners perceive speech quality in telecommunications. Such tools, called objective quality measures, are of great interest in the telecommunications industry to evaluate how new or deployed sy ...
In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge su ...
In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge su ...
This study proposes a semi-automatic approach aimed at detecting conflict in conversations. The approach is based on statistical techniques capable of identifying turn-organization regularities associated with conflict. The only manual step of the process ...
Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully ...
Recent studies have shown that speech recognizers may benefit from data in languages other than the target language through efficient acoustic model- or feature-level adaptation. Crosslingual Tandem-Subspace Gaussian Mixture Models (SGMM) are successfully ...
The neural correspondence between the systems responsible for the execution and recognition of actions has been suggested both in humans and non-human primates. Apart from being a key region of this visuo-motor observation-execution matching (OEM) system, ...