TASLP Volume 27 Issue 8

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs) trained to discriminate speakers, pass-phrases, and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved.

Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time–frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase...

One of the challenges in computational acoustics is the identification of models that can simulate and predict the physical behavior of a system generating an acoustic signal. Whenever such models are used for commercial applications, an additional constraint is the time to market, making automation of the sound design process desirable.

Deep neural networks (DNNs) have been proven to be powerful models for acoustic scene classification tasks. State-of-the-art DNNs have millions of connections and are computationally intensive, making them difficult to deploy on systems with limited resources.