Self-supervised speech models are known to encode phonological information, yet it remains underexplored whether they capture phonological properties characteristic of Japanese, such as mora and pitch accent. This paper introduces JPhonABX, a Japanese phonological discrimination dataset targeting moraic and accentual contrasts. It consists of 192,000 moraic and 98,000 accentual ABX test items, covering various types of contrasts such as those involving moraic nasal, geminate consonant, vowel length, and pitch accent. Our human evaluation by native Japanese speakers shows high discrimination accuracy and inter-rater agreement, validating the quality of the dataset. We further evaluate several self-supervised speech models and find that while almost all of them show no significant difference from humans in mora discrimination, they perform significantly worse in accent discrimination, and even models trained on Japanese fail to reach human-level performance.

This paper aims to forecast the implicit emotion elicited in the dialogue partner by a textual input utterance. Forecasting the interlocutor’s emotion is beneficial for natural language generation in dialogue systems to avoid generating utterances that make the users uncomfortable. Previous studies forecast the emotion conveyed in the interlocutor’s response, assuming it will explicitly reflect their elicited emotion. However, true emotions are not always expressed verbally. We propose a new task to directly forecast the implicit emotion elicited by an input utterance, which does not rely on this assumption. We compare this task with related ones to investigate the impact of dialogue history and one’s own utterance on predicting explicit and implicit emotions. Our result highlights the importance of dialogue history for predicting implicit emotions. It also reveals that, unlike explicit emotions, implicit emotions show limited improvement in predictive performance with one’s own utterance, and that they are more difficult to predict than explicit emotions. We find that even a large language model (LLM) struggles to forecast implicit emotions accurately.