As the most widely-used spatial filtering approach for multi-channel speech separation,
beamforming extracts the target speech signal arriving from a specific direction. An …

During the past three decades, the issue of processing spectral phase has been largely
neglected in speech applications. There is no doubt that the interest of speech processing …

The complete acoustic features include magnitude and phase information. However,
traditional speech emotion recognition methods only focus on the magnitude information …

Automatic speech recognition (ASR) plays a crucial role in facilitating natural and efficient
human–computer interaction. This paper offers a comprehensive review of ASR systems …

In conventional single-channel speech enhancement, typically the noisy spectral amplitude
is modified while the noisy phase is used to reconstruct the enhanced signal. Several recent …

Abstract Speech Emotion Recognition (SER) is an important task since emotion is a primary
dimension in human communication and health. It has a wide variety of practical …

Previous studies of speech emotion recognition utilize convolutional neural network (CNN)
directly on amplitude spectrogram to extract features. CNN combines with bidirectional long …

Conventional speech enhancement methods typically utilize the noisy phase spectrum for
signal reconstruction. This letter presents a novel method to estimate the clean speech …

Mainstream deep learning-based dysarthric speech detection approaches typically rely on
processing the magnitude spectrum of the short-time Fourier transform of input signals, while …

Automatic speech emotion recognition (SER) is a crucial task in communication-based
systems, where feature extraction plays an important role. Recently, a lot of SER models …