A Review on Emotion Recognition Algorithms using Speech Analysis <s> Figure 2. Typical Speech Emotion Recognition System <s> We present a method to classify fixed-duration windows of speech as expressing anger or not, which does not require speech recognition, utterance segmentation, or separating the utterances of different speakers and can, thus, be easily applied to real-world recordings. We also introduce the task of ranking a set of spoken dialogues by decreasing percentage of anger duration, as a step towards helping call center supervisors and analysts identify conversations requiring further action. Our work is among the very few attempts to detect emotions in spontaneous human-human dialogues recorded in call centers, as opposed to acted studio recordings or human-machine dialogues. We show that despite the non-perfect performance (approx. 70% accuracy) of the window-level classifier, its decisions help produce a ranking of entire conversations by decreasing percentage of anger duration that is clearly better than a random ranking, which represents the case where supervisors and analysts randomly select conversations to inspect. <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Figure 2. Typical Speech Emotion Recognition System <s> The identification of emotional hints from speech shows a large number of applications. Machine learning researchers have analyzed sets of acoustic parameters as potential cues for the identification of discrete emotional categories or, alternatively, of the dimensions of emotions. Experiments have been carried out over records including simulated or induced emotions, even if recently more research has been carried out on spontaneous emotions. However, it is well known that emotion expression depends not only on cultural factors but also on the individual and also on the specific situation. In this work we deal with the tracking of annoyance shifts during real phone-calls to complaint services. The audio files analyzed show different ways to express annoyance, as, for example, disappointment, impotence or anger. However variations of parameters derived from intensity combined with some spectral information and suprasegmental features have shown to be very robust for each speaker and annoyance rate. The work also discussed the annotation problem and proposed an extended rating scale in order to include annotators disagreements. Our frame classification results validated the annotation procedure. Experimental results also showed that shifts in customer annoyance rates could be potentially tracked during phone calls. <s> BIB002 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Figure 2. Typical Speech Emotion Recognition System <s> Emotions play a key role in cognitive processes, particularly in learning. Educators should know the emotional state of each student during a teaching activity. They must help students to experiment, interact and explore new topics and constructs. Students must feel in a state that maximize their performance. To know the emotional state of student, we need an emotion recognition system. It can be based on emotion recognition from speech (SER). SER is an important research area in humancomputer systems interaction (HCI). The major challenges for making a speech emotion recognition system are selecting the most suitable features and choosing the appropriate classification method. In this paper, we overview emotional speech recognition bearing in mind the role of emotions in learning. Here below we present briefly the obtained results by our SER system. <s> BIB003 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Figure 2. Typical Speech Emotion Recognition System <s> In year 2013, 777,817 out of 22,702,211 of registered vehicles were involved in road traffic crashes, with a damage cost of more than 9.3 billion ringgits in Malaysia. Automotive makers have deployed a wide range of active safety systems (traction control, auto cruise and braking, collision avoidance, and lane switching) in recent years. However, these systems failed to significantly reduce road crash statistics. The automotive network comprises the vehicle, road and driver. Huge effort had been placed on the vehicle and road, whereas the driver remains the feeblest part. Consequently, automotive makers should focus their attention on monitoring drivers' emotion, which influences their driving behaviour. This study aims to explore available technology that monitors driver behaviour, besides proposing accident-stimulating emotion that affects driving. Furthermore, a conceptual framework for recognising anger and stress emotion, which is a cause of road crash, is proposed. <s> BIB004
The application of SER can be targeted to several sectors. In banking, an auto caller equipped with SER may assist in detecting the emotion of the customer, generating custom responses based on the result BIB001 BIB002  . In education, an e-learning portal with SER can detect the emotions of the user such as frustration and stress, determining whether the studying is conducive or not and give appropriate countermeasures BIB003 . Yet another application is in transportation, where in the near-future that vehicles are capable of auto-driving, the system can take over the steering wheel in the case where an unhealthy amount of emotion is detected from the driver BIB004 .