During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker's existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems' varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap's prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

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With increased interest of human-computer/human-human interactions, systems deducing and identifying emotional aspects of a speech signal has emerged as a hot research topic. Recent researches are directed towards the development of automated and intelligent analysis of human utterances. Although numerous researches have been put into place for designing systems, algorithms, classifiers in the related field; however the things are far from standardization yet. There still exists considerable amount of uncertainty with regard to aspects such as determining influencing features, better performing algorithms, number of emotion classification etc. Among the influencing factors, the uniqueness between speech databases such as data collection method is accepted to be significant among the research community. Speech emotion database is essentially a repository of varied human speech samples collected and sampled using a specified method. This paper reviews 34 `speech emotion databases for their characteristics and specifications. Furthermore critical insight into the imitational aspects for the same have also been highlighted.

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The research community's ever-increasing interest in studying human-computer interactions (HCI), systems deducing, and identifying a speech signal's emotional aspects has emerged as a hot research topic. Speech Emotion Recognition (SER) has brought the development of automated and intelligent analysis of human utterances to reality.Typically, an SER system focuses on extracting the features from speech signals such as pitch frequency, formant features, energy-related and spectral features, tailing it with a classification quest to understand the underlying emotion. The key issues pivotal for a successful SER system are driven by the proper selection of proper emotional feature extraction techniques. In this paper, Mel-frequency Cepstral Coefficient (MFCC) and Teager Energy Operator (TEO) along with a new proposed Feature Fusion of MFCC and TEO referred to as Teager-MFCC (TMFCC) is examined over a multilingual database consisting of English, German and Hindi languages. Deep Neural Networks have been used to classify the different emotions considered, happy, sad, angry, and neutral. Evaluation results show that the proposed fusion TMFCC with a recognition rate of 92.7% outperforms TEO and MFCC. With TEO and MFCC configurations, the recognition rate has been found as 88.5% and 90.0%, respectively.

Speech is the most significant communication mode among human beings and a potential method for humancomputer interaction (HCI). Being unparallel in complexity, the perception of human speech is very hard. The most crucial characteristic of speech is gender, and for the classification of gender often pitch is utilized. However, it is not a reliable method for gender classification as in numerous cases, the pitch of female and male is nearly similar. In this paper, we propose a time-frequency method for the classification of gender-based on the speech signal. Various techniques like framing, Fast Fourier Transform (FFT), auto-correlation, filtering, power calculations, speech frequency analysis, and feature extraction and formation are applied on speech samples. The classification is done based on features derived from the frequency and time domain processing using the Support Vector Machines (SVM) algorithm. SVM is trained on two speech databases Berlin Emo-DB and IITKGP-SEHSC, in which a total of 400 speech samples are evaluated. An accuracy of 83% and 81% for IITKGP-SEHSC and Berlin Emo-DB have been observed, respectively.

Emotion recognition through speech has many potential applications, however the challenge comes from achieving a high emotion recognition while using limited resources or interference such as noise. In this paper we have explored the possibility of improving speech emotion recognition by utilizing the voice activity detection (VAD) concept. The emotional voice data from the Berlin Emotion Database (EMO-DB) and a custom-made database LQ Audio Dataset are firstly preprocessed by VAD before feature extraction. The features are then passed to the deep neural network for classification. In this paper, we have chosen MFCC to be the sole determinant feature. From the results obtained using VAD and without, we have found that the VAD improved the recognition rate of 5 emotions (happy, angry, sad, fear, and neutral) by 3.7% when recognizing clean signals, while the effect of using VAD when training a network with both clean and noisy signals improved our previous results by 50%.