Emotion Recognition from Hindi Speech using MFCC and Sparse DTW

Journal of Interdisciplinary Mathematics, 2020

Now a days speech is fastest medium of giving instruction to machines to do any task. When a person uttered a word at that time machine can understand the semantic of the utterance but not the emotion related with that utterance. This study mainly focused on combining on different types of speech features together and then this paper used various statistical techniques for reducing the dimensionality of data and then applying machine learning algorithm to train dataset and predicting the emotional state of the person so that while receiving the instruction form humans, machines can provide better response for recognizing emotion.

2013

Emotion based speaker Identification System is the process of automatically identifying speaker’s emotion based on features extracted from speech waves. This paper presents experiment with the building and testing of a Speaker’s emotion identification for Hindi speech using Mel Frequency Cepestral Coefficients and Vector Quantization techniques. We collected voice samples of Hindi speech sentences in four basic emotions to study speaker’s emotion identification and it was found that with proposed emo-voice model we are able to achieve accuracy of 73% of speaker’s emotion identification in speech out of 93% of the total speech samples provided to the system.

Advances in Intelligent Systems and Computing, 2015

This paper explore on emotion recognition from Marathi speech signals by using feature extraction techniques and classifier to classify Marathi speech utterances according to their emotional contains. A different type of speech feature vectors contains different emotions, due to their corresponding natures. In this we have categorized the emotions as namely Anger, Happy, Sad, Fear, Neutral and Surprise. Mel Frequency Cepstral Coefficient (MFCC) feature parameters extracted from Marathi speech Signals depend on speaker, spoken word as well as emotion. Gaussian mixture Models (GMM) is used to develop Emotion classification model. In this, recently proposed feature extraction technique and classifier is used for Marathi spoken words. In this each subject/Speaker has spoken 7 Marathi words with 6 different emotions that 7 Marathi words are Aathawan, Aayusha, Chamakdar, Iishara, Manav, Namaskar, and Uupay. For experimental work we have created total 924 Marathi speech utterances database and from this we achieved the empirical performance of overall emotion recognition accuracy rate obtained using MFCC and GMM is 84.61% rate of our Emotion Recognition for Marathi Spoken Words (ERFMSW) system. We got average accuracy for male and female is 86.20% and 83.03% respectively.

International Journal of Electrical and Computer Engineering (IJECE), 2020

In the last couple of years emotion recognition has proven its significance in the area of artificial intelligence and man machine communication. Emotion recognition can be done using speech and image (facial expression), this paper deals with SER (speech emotion recognition) only. For emotion recognition emotional speech database is essential. In this paper we have proposed emotional database which is developed in Gujarati language, one of the official's language of India. The proposed speech corpus bifurcate six emotional states as: sadness, surprise, anger, disgust, fear, happiness. To observe effect of different emotions, analysis of proposed Gujarati speech database is carried out using efficient speech parameters like pitch, energy and MFCC using MATLAB Software.

Journal of theoretical and applied information technology, 2020

In past few eras, emotion recognition from speech is one of the hottest research topic in the field of Human Computer Interaction. Many researches are going on various types of language, but for Bengali language, it is still very novice. In this work, 4 emotional state have been recognized i.e. happy, sad, angry and neutral from Bengali Speech Dataset. Proposed approach uses Pitch and Mel-frequency Cepstral Coefficient (MFCC) feature vectors to train k-Nearest Neighbor classifier for this work. A self-built Bengali emotional speech dataset has been used for both training and testing purpose. The dataset consists of consist of 50 people with 400 isolated emotional sentences. Using this dataset and above technique, we achieved 87.50% average accuracy rate, with detection accuracy each emotion (happy, sad, angry, neutral) respectively 80.00%, 75.00%, 85.00% and 75.00% in this work.

2015

Emotions play important roles in expressing feelings as it tend to make people acts differently. Determining emotions of the speaker is less complicated if we are facing him/her rather than from voice independently such as conversation in telephone. However it would be a great achievement if we able to detect with what emotion a speaker is speaking just by listening to the voice. This project is a small step towards it and we basically are focusing on determining emotions through the recorded speech and developing the prototype system. The ability to detect human emotion from their speech is going to be a great addition in the field of human-robot interaction. The aim of the work is to build an emotion recognition system using Mel-frequency cepstral coefficients (MFCC) and Gaussian mixture model (GMM). In this work four emotional states happy, sad, angry and neutral are taken for classification of emotions. Here we have considered only 10 speakers, 7 male and 3 female, all belonging to upper Assam region and all speak in the same accent. The experiments are performed for only speaker dependent and text independent case.

International Journal of Speech Technology, 2015

Emotions are broad aspects and expressed in a similar way by every human being; however, these are affected by culture. This creates a major threat to the universality of speech emotion detection system. Cultural behaviour of society affects the way emotions are expressed and perceived. Hence, an emotion recognition system customized for languages within a cultural group is feasible. In this work, a speaker dependent and speaker independent emotion recognition system has been proposed for two different dialects of Odisha: Sambalpuri and Cuttacki. Spectral speech features, such as, log power, Melfrequency cepstral coefficients (MFCC), Delta MFCC, Double delta MFCC, log frequency power coefficients, and linear predictive cepstral coefficients, are used with Hidden Markov model and support vector machines (SVM) classifier, for classifying a speech into one of the seven discrete emotion classes: anger, happiness, disgust, fear, sadness, surprise, and neutral. For a better comparative study of system's accuracy, features are taken individually as well as in combinations by varying sampling frequency, frame length and frame overlapping. Best average recognition accuracy obtained for speaker independent system, is 82.14 % for SVM classifier using only MFCC as feature vector. However, for speaker dependent system a hike in accuracy of more than 10 % is seen. It is also revealed that use of MFCC on SVM classifier, not only gives the best overall performance on 8 kHz sampling frequency, but also shows consistent performance for all the emotion classes, compared to other classifiers and feature combinations with less computational complexity. Hence, it can be applied efficiently in call centre application for emotion recognition over telephone.

International Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022

Emotion recognition is a significant part in designing a better Human-Computer Interaction (HCI) System. Increasing applications of Emotion recognition from speech has been found in Education, gaming, medicine and automobiles industries. And after the Covid-19 pandemic identification of human emotions and action upon is the need of the hour. In this paper we try to identify four emotions by training a CNN model using Manipuri speech data set. We test our model using real time speech signal. Our model identifies sad emotion accurately but fails to identify angry emotion.

International Journal of Computer Applications, 2018

A modern development in technology is Speech Emotion Recognition (SER). SER in partnership with Humane-Machine interaction (HMI) has advanced machine intelligence. An emotion precise HMI is designed by integrating speech processing and machine learning algorithm which is sculpted to formulate an automated smart and secure application for detecting emotions in a household as well as in commercial application. This project presents a study of distinguishing emotions by acoustic speech recognition (ASR) using K-means nearest neighbor (K-NN), a machine learning (ML) technique. The most significant paralinguistic information obtained from spectral features is presented by ASR i.e. by using Mel frequency cepstrum coefficient (MFCC). The most important processing techniques methods include feature extraction, feature selection, and classification of emotions. A customized dataset consisting of speech corpus, simulated emotion samples in the Sanskrit language is used to classify emotions in different emotional classes i.e. happy, sad, excitement, fear, anger and disgust. The emotions are classified using a K-NN algorithm over 2 separate models, based on the soft and high pitch voice. Model 1 and 2 achieved about 72.95% and 76.96% recognition

National Academy Science Letters, 2020

Speech emotion-related analysis needs good emotion corpus. The construction and evaluation of two Tamil emotion corpora, one for children and the other for the adults, are described here. Children emotion speech samples are collected from 30 Tamil movies, and the length of the utterances varies from 5 to 40 s. Tamil audio plays are the resources for building the adult emotion corpus. The emotion prosodies are collected, segmented and annotated for the categories of anger, happy, sad and neutral emotions. Observers' perception test result has been used to evaluate the annotation of emotion. Automatic emotion classification systems have been built using Gaussian Mixture Model and Support Vector Machine. The database is created with an objective of the acoustic investigation of emotion expression in Tamil, analyzing the influence of speaker's culture and age on emotion expression and investigating the requirement of the need of features unique to Tamil speech on various automatic analysis of speech like emotion recognition, speaker recognition, etc.