5 accepted papers
Prior work on multilingual sentence embedding has demonstrated that the efficient use of natural language inference (NLI) data to build high-performance models can outperform conventional methods. However, the potential benefits from the recent “exponential” growth of language models with billions o…
Following the success in language domain, the self-attention mechanism (Transformer) has been adopted in the vision domain and achieving great success recently. Additionally, the use of multi-layer perceptron (MLP) is also explored in the vision domain as another stream. These architectures have bee…
In neural network-based monaural speech separation techniques, it has been recently common to evaluate the loss using the permutation invariant training (PIT) loss. However, the ordinary PIT requires to try all N! permutations between N ground truths and N estimates. Since the factorial complexity e…
In Japanese text-to-speech (TTS), it is necessary to add accent information to the input sentence. However, there are a limited number of publicly available accent dictionaries, and those dictionaries e.g. UniDic, do not contain many compound words, proper nouns, etc., which are required in a practi…
This paper describes a novel text-to-speech (TTS) technique based on deep convolutional neural networks (CNN), without use of any recurrent units. Recurrent neural networks (RNN) have become a standard technique to model sequential data recently, and this technique has been used in some cutting-edge…