11 accepted papers
We propose Evidential-TTS, a novel zero-shot text-to-speech (TTS) system based on evidential deep learning (EDL). The model includes a length regulator to ensure precise alignment between phonemes and acoustic tokens. This module allows the evidential token generator to convert the aligned phoneme s…
Recently, fake audio detection has gained significant attention, as advancements in speech synthesis and voice conversion have increased the vulnerability of automatic speaker verification (ASV) systems to spoofing attacks. A key challenge in this task is generalizing models to detect unseen, out-of…
We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning. In contrast to conventional methods, it is trained with oracle guidance, which is derived from the target sequence. Since the oracle guida…
Deep speaker embedding systems are usually trained with classification-based or end-to-end learning objectives. Popular end-to-end approaches utilize deep metric learning, which can be viewed as a few-shot classification objective. In this paper, we investigate the limit of conventional learning obj…
In time-series forecasting, signals such as traffic volume collected in the real world are noisy and irregularly sampled due to sensor malfunctions, so it is difficult to make accurate prediction. To resolve such difficulty, downsampling can be used to reduce noise and allow capturing slow trend of…
Knowledge distillation (KD) approach is widely used in the deep learning field mainly for model size reduction. KD utilizes soft labels of teacher model, which contain the dark- knowledge that one-hot ground-truth does not have. This knowledge can improve the performance of already saturated student…
For multi-channel speech recognition, speech enhancement techniques such as denoising or dereverberation are conventionally applied as a front-end processor. Deep learning-based front-ends using such techniques require aligned clean and noisy speech pairs which are generally obtained via data simula…
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension. Therefore, flow-based models cannot be adequately trained if the dimension of the data distribution does not match that of the underlying target distribution. In this paper, we…
Since the introduction of deep neural network (DNN)-based acoustic model, robust automatic speech recognition using DNN are being in research. Especially in model adaptation, the techniques utilizing auxiliary context features is known to be a promising technique. Recently, we proposed a technique w…
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…
Ever since the deep neural network (DNN)-based acoustic model appeared, the recognition performance of automatic speech recognition has been greatly improved. Due to this achievement, various researches on DNN-based technique for noise robustness are also in progress. Among these approaches, the noi…