Acoustic richness modulates the neural networks supporting intelligible speech processing

Neural networks for speech processing

Phase Entrainment of Brain Oscillations Causally Modulates Neural Responses to Intelligible Speech

Due to their periodic nature, neural oscillations might represent an optimal "tool" for the processing of rhythmic stimulus input [1-3]. Indeed, the alignment of neural oscillations to a rhythmic stimulus, often termed phase entrainment, has been repeatedly demonstrated [4-7]. Phase entrainment is central to current theories of speech processing [8-10] and has been associated with successful sp...

Temporal Processing Neural Networks for Speech Recognition

Application of the temporal processing neural networks (TPNNs) to the speech recognition is justified by the nature of the task. Indeed ASR is a sequence recognition problem and assumes incorporation of time into decision process. Static models treat elements of sequence as independent patterns, which is clearly unrealistic. On the other hand temporal processing nets, built on the basis of mult...

Speech Processing Using Artificial Neural Networks

A three layer perceptron network is used to classify the /i/ sound using isolated words from diierent speakers. A classiication accuracy of 97% has been achieved. A map of phonemes is used to trace trajectories of utterances using the self-organising neural network. A crinkle factor is proposed which allows using the self-organising map to determine the inherent dimensionality of a set of point...

Is speech enhancement pre-processing still relevant when using deep neural networks for acoustic modeling?

Using deep neural networks (DNNs) for automatic speech recognition (ASR) has recently attracted much attention due to the large performance improvement they provide for a variety of tasks. DNNs are known to be robust to overfitting and to be able to remove speaker variability. Another important cause of variability in speech is the presence of noise. A lot of research has been undertaken on noi...