Design and Development of a Multi-Lingual Speech Corpora (TaMaR-EmoDB) for Emotion Analysis

Abstract

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.

Automatic Speech Emotion Recognition of Younger School Age Children

“…This is consistent with the results of perception tests for adult emotion speech. For example, Rajan et al [13] reported comparable From Figure 2 and Table 4 we can see that experts recognize all emotions noticeably above chance (0.25 for 4-class classification). This is consistent with the results of perception tests for adult emotion speech.…”

“…For example, Sowmya and Rajeswari [75] reported that they achieved an overall accuracy of 0.85 for automatic children's speech emotion recognition in the Tamil language with an SVM classifier on prosodic (energy) and spectral (MFCC) features. Rajan et al [13] reported that they achieved an Average Recall of 0.61 and Average Precision of 0.60 in the Tamil language using a DNN framework, also on prosodic and spectral features.…”

“…This is consistent with the results of perception tests for adult emotion speech. For example, Rajan et al [13] reported comparable results of subjective evaluation using a perception test with an overall accuracy of 0.846 in recognizing emotional states-happiness, anger, sadness, anxiety, and neutral-from acted emotional speech in the Tamil language.…”

“…Humans are able to identify emotional states in Russian children's emotional speech above chance. Following [13], this ensures the emotional quality and naturalness of the emotions in the collected corpus of Russian children's emotional speech. 2.…”

“…Another severe problem is the paucity of publicly available transcribed linguistic resources for children's speech. Moreover, research on SER faces the problem that most available speech corpora differ from each other in important ways, such as methods of annotation and scenarios of interaction [11][12][13]. Inconsistencies in these methods and scenarios make it difficult to build SER systems.…”