  Wake-up words (WUW) is a short sentence used to activate a speech recognition
system to receive the user's speech input. WUW utterances include not only the
lexical information for waking up the system but also non-lexical information
such as speaker identity or emotion. In particular, recognizing the user's
emotional state may elaborate the voice communication. However, there is few
dataset where the emotional state of the WUW utterances is labeled. In this
paper, we introduce Hi, KIA, a new WUW dataset which consists of 488 Korean
accent emotional utterances collected from four male and four female speakers
and each of utterances is labeled with four emotional states including anger,
happy, sad, or neutral. We present the step-by-step procedure to build the
dataset, covering scenario selection, post-processing, and human validation for
label agreement. Also, we provide two classification models for WUW speech
emotion recognition using the dataset. One is based on traditional hand-craft
features and the other is a transfer-learning approach using a pre-trained
neural network. These classification models could be used as benchmarks in
further research.
