Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data is available. In this study, we investigate the problem of cross-lingual emotion recognition for Urdu language and contribute URDU---the first ever spontaneous Urdu-language speech emotion database. Evaluations are performed using three different Western languages against Urdu and experimental results on different possible scenarios suggest various interesting aspects for designing more adaptive emotion recognition system for such limited languages. In results, selecting training instances of multiple languages can deliver comparable results to baseline and augmentation a fraction of testing language data while training can help to boost accuracy for speech emotion recognition. URDU data is publicly available for further research.
To study the universality of emotional cues among languages, @cite_6 studied speech emotion recognition for Mandarin vs. Western languages (i.e., German, and Danish). They evaluated gender-specific speech emotion classification and achieved the accuracy more than the chance level. In @cite_9 , authors used six emotional databases and evaluated different scenarios for cross-corpus speech emotion recognition. They were able to capture the limitations of current systems due to their very poor performance on spontaneous set or natural emotional corpus. In another interesting work, @cite_15 developed an emotion profile based ensemble SVM for emotion recognition in different unseen languages. The authors used the RML emotion database that covers six languages. However, this data is recorded under the same condition and contains very small number of utterances, five sentences, for each emotion.
@cite_9 @cite_6 @cite_15
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An investigation on classification of emotional speech cross different language families is proposed in this paper. Datasets on three languages, CDESD in Mandarin, Emo-DB in German, and DES in Danish are analyzed. With 2-D classifications on arousal-appraisal space, better recognition performances are observed in arousal dimension than in appraisal dimension. The classification rates in cross language family test between CDESD and Emo-DB or DES are far higher than chance level, shows that there exist universal mechanisms in human voice emotion independent on languages. Results in test within the same language family between Emo-DB and DES are even better than in cross language family test with CDESD in Mandarin, shows the language and culture also influence the way of expression in speech. The best classification rate in the cross language family test is achieved on male speech samples as 71.62 , when CDESD dataset is used as training set and Emo-DB as testing set. Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to model each language independently, preserving the cultural properties. In a second stage, we use the concept of universality of emotions to map and predict emotions in never-seen languages. Features and classifiers widely tested for similar tasks were used to set the baselines. We developed a novel ensemble classifier to deal with multiple languages and tested it on never-seen languages. Furthermore, this ensemble uses the Emotion Profiles technique in order to map features from diverse languages in a more tractable space. The experiments were performed in a language-independent scheme. Results show that the proposed model improves the baseline accuracy, whereas its modular design allows the incorporation of a new language without having to train the whole system.