6 accepted papers
Diffusion probabilistic models have shown impressive performance for speech enhancement, but they typically require 25 to 60 function evaluations in the inference phase, resulting in heavy computational complexity. Recently, a fine-tuning method was proposed to correct the reverse process, which sig…
The streaming Sound EnhAncement Network (SEANet) has demonstrated impressive performance for speech bandwidth extension (BWE) with low latency and computational complexity. Although the streaming SEANet was designed for voice communication systems, it was not tested with decoded signals that include…
Time-domain approaches have shown the potential to improve the performance of speaker verification, but still predominant approaches utilize hand-crafted features such as the mel filterbank energies. Although these features are based on speech perception models and exhibited impressive performances,…
Recently, speaker verification systems using deep neural networks have been widely studied. Many of them utilize hand-crafted features such as mel-filterbank energies, mel-frequency cepstral coefficients, and magnitude spectrograms, which are not designed specifically for the speaker verification ta…
In this paper, we propose a novel emotion recognition method to reflect affect salient information using acoustic and lexical features. The acoustic features are extracted from the speech signal by applying statistical functionals of emotionally high-level features derived from Deep Neural Network (…
Non-negative matrix factorization (NMF) is an unsupervised technique to represents a nonnegative data matrix with a product of nonnegative basis and encoding matrices. The encoding matrix for the training phase contains information on the pattern of how each basis vector is utilized. The histogram f…