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Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Clinical literature provides convincing evidence that language deficits in Alzheimer's disease (AD) allow for distinguishing patients with dementia from healthy subjects. Currently, computational approaches have widely investigated lexicosemantic aspects of discourse production, while pragmatic aspects like cohesion and coherence, are still mostly unexplored.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Speech analysis could provide an indicator of Alzheimer's disease and help develop clinical tools for automatically detecting and monitoring disease progression. While previous studies have employed acoustic (speech) features for characterisation of Alzheimer's dementia, these studies focused on a few common prosodic features, often in combination with lexical and syntactic features which require transcription.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Utilizing a human-perception-related objective function to train a speech enhancement model has become a popular topic recently. The main reason is that the conventional mean squared error (MSE) loss cannot represent auditory perception well. One of the typical human-perception-related metrics, which is the perceptual evaluation of speech quality (PESQ), has been proven to provide a high correlation to the quality scores rated by humans.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
Developing Semi-Supervised Seq2Seq (S4) learning for sequence transduction tasks in natural language processing (NLP), e.g. semantic parsing, is challenging, since both the input and the output sequences are discrete. This discrete nature makes trouble for methods which need gradients either from the input space or from the output space.
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
In this letter, we propose a new approach to tracking a target that maneuvers based on the multiple-constant-turns model. Usually, the interactive-multiple-model (IMM) algorithm based on the extended Kalman filter (IMM-EKF) is employed for this problem with successful tracking performance.
Section: Publications & Resources
This article addresses the problem of distance estimation using binaural hearing aid microphones in reverberant rooms. Among several distance indicators, the direct-to-reverberant energy ratio (DRR) has been shown to be more effective than other features. Therefore, we present two novel approaches to estimate the DRR of binaural signals.
Section: Publications & Resources
Recurrent neural networks (RNNs) can predict fundamental frequency (F 0 ) for statistical parametric speech synthesis systems, given linguistic features as input. However, these models assume conditional independence between consecutive F 0 values, given the RNN state. In a previous study, we proposed autoregressive (AR) neural F 0 models to capture the causal dependency of successive F 0 values.
Section: Publications & Resources
Differential microphone arrays (DMAs) often encounter white noise amplification, especially at low frequencies. If the array geometry and the number of microphones are fixed, one can improve the white noise amplification problem by reducing the DMA order. With the existing differential beamforming methods, the DMA order can only be a positive integer number.
Section: Publications & Resources
This work presents a method that persuades acoustic reflections to be a favorable property for sound source localization. Whilst most real world spatial audio applications utilize prior knowledge of sound source position, estimating such positions in reverberant environments is still considered to be a difficult problem due to acoustic reflections.
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