6 accepted papers
Building ASR systems robust to foreign-accented speech is an important challenge in today's globalized world. A prior study explored the way to enhance the performance of phonetic token-based ASR on accented speech by reproducing the phenomenon known as interlanguage speech intelligibility benefit (…
In this study, we investigate whether speech symbols, learned through deep learning, follow Zipf’s law, akin to natural language symbols. Zipf’s law is an empirical law that delineates the frequency distribution of words, forming fundamentals for statistical analysis in natural language processing.…
Speech emotion recognition (SER) is a challenging task whose performance heavily relies on suitable affect-salient representations. Recently, transformer has exhibited outstanding qualities in learning relevant representations associated with this task. However, a normal transformer is only able to…
Discriminative nonnegative matrix factorization (DNMF) has been investigated as a promising basis-learning method for monaural source separation. To the best of our knowledge, however, no good and sound discussion has been made on quantitative definition of discriminability and it is difficult to ev…
In this paper, we propose a method to estimate statistical divergence between probability distributions by a DNN-based discriminative approach and its use for language identification tasks. Since statistical divergence is generally defined as a functional of two probability density functions, these…
This paper presents the first version of a speaker verification spoofing and anti-spoofing database, named SAS corpus. The corpus includes nine spoofing techniques, two of which are speech synthesis, and seven are voice conversion. We design two protocols, one for standard speaker verification evalu…