This paper presents the design, the development of a new multilingual emotional speech corpus, TaMaR-EmoDB (Tamil Malayalam Ravula-Emotion DataBase) and its evaluation using a deep neural network (DNN)-baseline system. The corpus consists of utterances from three languages, namely, Malayalam, Tamil and Ravula, a tribal language. The database consists of short speech utterances in four emotions-anger, anxiety, happiness, and sadness, along with neutral utterances. The subset of the corpus is first evaluated using a perception test, in order to understand how well the emotional state in emotional speech is identified by humans. Later, machine testing is performed using the fusion of spectral and prosodic features with DNN framework. During the classification phase, the system reports an average precision of 0.78, 0.60, 0.61 and recall of 0.84, 0.61 and 0.53 for Malayalam, Tamil, and Ravula, respectively. This database can potentially be used as a new linguistic resource that will enable future research in speech emotion detection, corpus-based prosody analysis, and speech synthesis.

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