A Review on Emotion Recognition Algorithms using Speech Analysis <s> REVIEW ON SPEECH EMOTION DATABASE <s> Emotion recognition grows to an important factor in future media retrieval and man machine interfaces. However, even human deciders often experience problems realizing one's emotion, especially of strangers. In this work we strive to recognize emotion independent of the person concentrating on the speech channel. Single feature relevance of acoustic features is a critical point, which we address by filter-based gain ratio calculation starting at a basis of 276 features. As optimization of a minimum set as a whole in general saves more extraction effort, we furthermore apply an SVM-SFFS wrapper based search. For a more robust estimation we also integrate spoken content information by a Bayesian net analysis of ASR outputs. Overall classification is realized in an early feature fusion by stacked ensembles of diverse base classifiers. Tests ran on a 3,947 movie and automotive interaction dialog-turns database consisting of 35 speakers. Remarkable overall performance can be reported in the discrimination of the seven discrete emotions named in the MPEG-4 standard with added neutrality <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> REVIEW ON SPEECH EMOTION DATABASE <s> 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. <s> BIB002 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> REVIEW ON SPEECH EMOTION DATABASE <s> In this paper, we propose to use a kernel sparse representation based classifier (KSRC) for the task of speech emotion recognition. Further, the recognition performance using the KSRC is improved by imposing a group sparsity constraint. The speech utterances with same emotion may have different duration, but the frame sequence information does not play a crucial role in this task. Hence, in this work, we propose to use dynamic kernels which explicitly models the variability in duration of speech signals. Experimental results demonstrate that, given a suitable kernel, KSRC with group sparsity constraint performs better as compared to the state-of-the-art support vector machines (SVM) based classifiers. <s> BIB003 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> REVIEW ON SPEECH EMOTION DATABASE <s> Speech emotion recognition is a still challenging problem despite having been investigated over the last couple of decades. Conventional speech emotion recognition performance is low, but this may be improved by considering new features and an annotation method. In this paper, firstly we use glottal features for speech emotion recognition to improve its performance because the emotions are related to glottal characteristics. Secondly emotional labels used as “correct” emotions are usually annotated by multiple annotators in the conventional approach, but this may be ambiguous due to the confusion created by using each annotator's criterion. In this paper, a single annotator gives emotional labels in an aim to obtain a unified criterion and we use these labels for speech emotion recognition to improve its performance. Finally, context information which is included in a speech segment before the utterance being annotated, will be considered. <s> BIB004
To complete the process of SER, the system requires a database for training and testing. An emotion database generally consists of various audio recordings that are labeled their appropriate emotion. For this section, the discussion will be directed towards the number of databases used, the method of obtaining the dataset, the variety of emotions categorized, as well as the challenges that most researches have in obtaining these databases. Usually a single SER system will rely only on a single database, to reduce data variance due to external factors such as different accents. While most systems are supported by one database, there are some researches that utilizes more, such as by BIB003 , that have used the Berlin emotional speech database (EMO-DB) in combination with the German FAU Aibo emotion corpus (FAUAEC). With that said, these databases are still only using one language; German. As previously mentioned, there are external factors that can affect the speech features that are extracted. The closest attempt of integrating multiple databases was performed by BIB002 by using 6 standard databases (AVIC, DES, EMO-DB, eNTERFACE, SmartKom, SUSAS) in a cross-corpora and multilingual evaluation experiment. An alternative is using a database that has already integrated multiple languages, such as the INTERFACE corpus, which supports English, Slovenian, Spanish, and French. Another aspect to consider is how these speech emotion data are obtained. One may debate that true authentic emotion can only be captured at the moment, but spontaneous speech is difficult to record. To ensure proper speech processing, the system requires better audio quality. This is simply not feasible to attain without proper sound recording setup and environment. Therefore, the most used method is for professional or experienced actors to express the emotion through acting, then labeling each speech segment on its appropriate category. The EMO-DB and LDC Emotional Prosody Speech and Transcripts are two examples of an actor-based database. Generally, this is conducted under ideal conditions (ie: in a studio with minimum noise interference). Another interesting method of collecting data is by collecting the speech from existing media, such as from movies, television recording, etc. While the source can be still considered as a "professional actor", the method of collecting the data differs from the first but maintains the general quality of audio. This however is met with the problem of copyright of fair usage. An example of a research that utilizes this method is by BIB001 . Finally, there are researches that collect their data from non-professional actors. These databases are generally self-made from the local environment. But while a home-made database creation may be more convenient for the researcher, it becomes difficult to benchmark the results with other papers. There are variations of emotions that are categorized. The German Database for example, groups the emotion into anger, boredom, disgust, fear, happy, neutral, and sad. The more emotions category the database has, the more challenging it is for the SER system to achieve high accuracy. To solve this, some researches such as BIB004 merges and omits certain emotions with similar attributes, eg, the emotion of "disgust" and "anger". and focuses on those emotions with distinct variations. There are various other factors to consider when choosing the appropriate database such as number of actors, language, ethnicity, word utterance or whole sentence, but one factor that has been a deterrent for some young researchers is the fact that some databases are obscured by a pay wall. This leads to either creating their own database or using open-source databases available. Table 3 shows various databases along with the audio features and classifiers that are used by other researchers. 