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One of the problems with automated audio captioning (AAC) is the indeterminacy in word selection corresponding to the audio event/scene. Since one acoustic event/scene can be described with several words, it results in a combinatorial explosion of possible captions and difficulty in training. To solve this problem, we propose a Transformer-based audio-captioning model with keyword estimation called TRACKE. It simultaneously solves the word-selection indeterminacy problem with the main task of AAC while executing the sub-task of acoustic event detection/acoustic scene classification (i.e., keyword estimation). TRACKE estimates keywords, which comprise a word set corresponding to audio events/scenes in the input audio, and generates the caption while referring to the estimated keywords to reduce word-selection indeterminacy. Experimental results on a public AAC dataset indicate that TRACKE achieved state-ofthe-art performance and successfully estimated both the caption and its keywords.

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This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize longrange interactive information extracted from both target speaker's and interlocutor's utterances. In the proposed method, we combine multiple time-asynchronous long short-term memory recurrent neural networks, which can capture target speaker's and interlocutor's multiple sequential features, and their interactions. On the assumption of applying the proposed method to spoken dialogue systems, we introduce target speaker's acoustic sequential features and interlocutor's linguistic sequential features, each of which can be extracted in an online manner. Our evaluation confirms the effectiveness of taking dialogue context formed by the target speaker's utterances and interlocutor's utterances into consideration.