You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that is novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and SVM baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.

Cited Abstract(s):
[1]: As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: Acted data is often used rather than spontaneous data, results are reported on preselected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. Even speaker disjunctive evaluation can give only a little insight into the generalization ability of today's emotion recognition engines since training and test data used for system development usually tend to be similar as far as recording conditions, noise overlay, language, and types of emotions are concerned. A considerably more realistic impression can be gathered by interset evaluation: We therefore show results employing six standard databases in a cross-corpora evaluation experiment which could also be helpful for learning about chances to add resources for training and overcoming the typical sparseness in the field. To better cope with the observed high variances, different types of normalization are investigated. 1.8 k individual evaluations in total indicate the crucial performance inferiority of inter to intracorpus testing.

[2]: 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.

[3]: 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.

[4]:
Although cross-language and cross-corpus speech emotion recognition is an interesting problem, relatively few studies have addressed this topic. Existing studies have mostly studied the preliminary feasibility of cross-corpus learning and pointed to the need for further in-depth research. For example, [1] used six different corpora to analyse cross-corpora emotion recognition using support vector machines (SVM) and highlighted the limitations of current systems for cross-corpus emotion recognition. [4] used four corpora to evaluate some pilot experiments on cross-corpus emotion recognition while using SVM. They used three datasets for training and a fourth for testing, and showed that the cross-corpus emotion recognition is feasible. To explore the universal cues of emotions across languages, [3] investigated cross-language emotion recognition for Mandarin vs. Western languages (i.e., German, and Danish). The authors focused on gender-specific speech emotion recognition and achieved the classification rates higher than the chance level but less than baseline accuracy. [2] developed an ensemble SVM for emotion detection with a focus on emotion recognition in unseen languages.