Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.

This work focuses on the problem of detecting fake audio clips. To improve current audio spoofing detection models, we propose a selection of multiple audio augmentations spe-cially designed to resemble audio spoofing attacks. These augmentations are experimentally found to be very useful and using them achieves a notable performance of 2.8% EER on the ASVspoof 2019 challenge evaluation set. Unlike the widely employed acoustic features, in this paper we explore the use of Mel-spectrogram image features and employ vari-ous audio codecs to achieve robustness to codec and transmission channel variability present in the ASVspoof2021 Evalu-ation set. To better handle spectral information, crucial to de-tect spoofing, we adopt the WaveletCNN and VGG16 archi-tectures which outperform all baselines. Finally, we find that robustness of countermeasure systems degrades dramatically when provided with speech samples degraded through VoIP network transmission or mismatching audio compression.

Over the recent years, various self-supervised embedding learning methods for deep speaker verification were proposed. The performance of the self-supervised learning framework highly depends on the data augmentation technique, but due to the sensitive nature of speaker information within the speech signal, most speaker embedding training relies on simple augmentations such as additive noise or simulated reverberation. Thus while the conventional self-supervised speaker embedding systems can yield minimum within-utterance variability, their capability to generalize to out-of-set utterance is limited. In order to alleviate this problem, we investigate the utilization of the instance mix (i-mix) regularization for training a self-supervised speaker embedding system. Moreover, we propose a new mixup strategy that applies i-mix on the latent space, instead of the raw acoustic feature domain. In this paper, the i-mix and the proposed l-mix strategy were incorporated into the self-supervised angular prototypical and softmax-based objective functions and were evaluated on the VoxCeleb dataset. From the experimental results, we observe that the self-supervised embedding network can benefit greatly from the i-mix and l-mix strategies in terms of training stability and speaker verification performance.

Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are known to suffer from severe performance degradation when dealing with speech samples with different conditions (e.g., recording devices, emotional states). In this paper, we propose a novel fully supervised training method for extracting a speaker embedding vector disentangled from the variability caused by the nuisance attributes. The proposed framework was compared with the conventional deep learning-based embedding methods using the RSR2015 and VoxCeleb1 dataset. Experimental results show that the proposed approach can extract speaker embeddings robust to channel and emotional variability.

Recently, attention-based encoder-decoder (AED) models have shown state-of-the-art performance in automatic speech recognition (ASR). As the original AED models with global attentions are not capable of online inference, various online attention schemes have been developed to reduce ASR latency for better user experience. However, a common limitation of the conventional softmax-based online attention approaches is that they introduce an additional hyperparameter related to the length of the attention window, requiring multiple trials of model training for tuning the hyperparameter. In order to deal with this problem, we propose a novel softmax-free attention method and its modified formulation for online attention, which does not need any additional hyperparameter at the training phase. Through a number of ASR experiments, we demonstrate the tradeoff between the latency and performance of the proposed online attention technique can be controlled by merely adjusting a threshold at the test phase. Furthermore, the proposed methods showed competitive performance to the conventional global and online attentions in terms of word-error-rates (WERs).