  Speech emotion analysis is an important task which further enables several
application use cases. The non-verbal sounds within speech utterances also play
a pivotal role in emotion analysis in speech. Due to the widespread use of
smartphones, it becomes viable to analyze speech commands captured using
microphones for emotion understanding by utilizing on-device machine learning
models. The non-verbal information includes the environment background sounds
describing the type of surroundings, current situation and activities being
performed. In this work, we consider both verbal (speech commands) and
non-verbal sounds (background noises) within an utterance for emotion analysis
in real-life scenarios. We create an indigenous dataset for this task namely
"Indian EmoSpeech Command Dataset". It contains keywords with diverse emotions
and background sounds, presented to explore new challenges in audio analysis.
We exhaustively compare with various baseline models for emotion analysis on
speech commands on several performance metrics. We demonstrate that we achieve
a significant average gain of 3.3% in top-one score over a subset of speech
command dataset for keyword spotting.
