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

Indian EmoSpeech Command Dataset: A dataset for emotion based speech recognition in the wild