## 3 Detection Approaches Review
\begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Dataset or Datasets** & **Partitions** \\ \hline FAU AEC database: based on the FAU & FAU AIBO emotion corpus, which contains recordings of children interacting with a pet robot in German speech. In the training set there are 6601 instances of positive and 3358 negative valence, and in the test set 5792 positive 2465 negative valence; TUM Audio-Visual Interest Corpus (TUM AVIC), Berlin Emotional Speech Database (EMO-DB), eNTERFACE, Speech Under Simulated and Actual Stress (SUSAS), and the “Vera am Mittag” (VAM) database. The age, language, kind of speech, emotion type, number of positive and negative utterances and sampling rate are: children, German, variable, natural, 5823, 12393 and 16 kHz for FAU AIBO; adults, English, variable, natural, 553, 2449 and 44 kHz for TUM AVIC; adults, German, fixed, acted, 352, 142 and 16 kHz for EMO-DB; adults, English, fixed, induced, 855, 422 and 16 kHz for ENTERFACE; adults, English, fixed, natural, 1616, 1977 and 8 kHz for SUSAS; adults, German, variable, natural, 876, 71 and 16 kHz for VAM. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Datasets used and partitions for training and testing of the detection approaches (continuation). \begin{table}
\begin{tabular}{c c} \hline \hline
**Reference** & **Dataset or Datasets** & **Partitions** \\ \hline EMODB database: Ten professional & speakers for ten german sentences, \\
535 speech files, seven emotions (anger, & anxiety, boredom, disgust, happiness, neutral and sadness), recorded at & \\
48 kHz; IEMOCAP database: & Audio-visual data in English, \\ only audio track considered for this & work, five male speakers and five & \\ female speakers, six emotions of the & IEMOCAP database are considered & \\ (anger, excited, frustration, happiness, neutral and sadness), recorded at & \\
16 kHz; FAU AIBO database: & data is used for validation purposes, \\ spontaneous emotional speech, & and the remaining children’s data is & used for training purposes. \\  & (21 male and 30 female) at the age of 10–13 years interacting with a pet & \\ robot. Contains 9959 training chunks & \\ and 8257 testing chunks with length & \\ approximately 1.7 s. Chunks are & \\ categorized into five different emotions & (anger, emphatic, neutral, positive, & \\ and rest). & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Dataset or Datasets** & **Partitions** \\ \hline  & Corpus of 3,268 textual review & \\  & summaries produced by 384 annotators, & \\  & resulting in 1055 rated as negative, 1600 & \\  & as positive, and 613 as mixed; & \\  & CitySearch dataset: 87000 reviews & \\  & describing more than 6000 restaurant & \\  & businesses from the citysearch.com & \\  & website; AMT text dataset: short text & \\  & reviews summarized by Amazon & \\  & turkers; GoodRec dataset: a set of short & \\  & restaurant and bar recommendations & \\  & mined from the goodrec.com website; & \\  & short reviews of restaurants dataset: 84 & \\  & participants made short reviews of & \\  & restaurants on phone, answering & \\  & questions, rating them and making a & \\  & short free review. Resulted in & \\  & 52 positive and 32 negative reviews. & \\ \hline  & & The VAM corpus was used for all the \\  & VAM corpus: consists of audio-visual & experiments. 245 utterances of 20 \\  & spontaneous speech. signals were & \\  & sampled at 16 kHz and 16 bit resolution. & \\  & Facial image sequences were taken at a & \\  & rate of 25 fps. Labeled with emotion by & \\  & human listeners. The signals were & \\  & sampled at 16 kHz & \\  & & \\  & & \\  & & \\  & Database for Emotion Analysis using & \\  & Physiological Signals (DEAP): & \\  & physiological signals & \\  & Electroencephalogram (EEG) and & \\  & Peripheral physiological signals (PERI) & \\  & are used. EEG was recorded from 32 & \\  & active electrodes (32 channels).