An Improved Hindi Speech Emotion Recognition System

This paper deals with a novel approach towards Automatic Emotion Classification from human utterances. Discrete Wavelet Transform (DWT) is used for feature extraction from speech signals. Malayalam (One of the south Indian languages) is used for the experiment. We have used an elicited dataset of 500 utterances recorded from 10 male and 8 female speakers. Using Artificial Neural Network we have classified the four emotional classes such as neutral, happy, sad and anger correctly. A classification accuracy of 70% is obtained from this work

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

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.

International Journal of Computer Applications, 2015

Recognizing emotions from speech is a tuff task as we are not aware of the features which will accurately classify the emotions. This paper is an approach to show which speech feature classifies the emotions more accurately. The features compared here are Pitch and Formant while the classifier used is Linear Discriminant Analysis (LDA). The database used in this experiment was developed using 50 male and 50 female Marathi speaking native speakers. The emotions used here are Neutral, Happy, Sad, Surprise and Boredom. At the end of the experiment it was observed that formant recognized the emotions very efficiently and accurately with respect to that of energy.

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.

International Journal of Information Technology, 2018

This paper presents a study related to perceptual evaluation of emotions expressed in Hindi speech and their acoustic prosodic correlates. Six emotions i.e. Neutral, Happiness, Sad, Fear, Anger and Surprise were selected for the present study. For this purpose, a database of fifteen continuous sentences and isolated words i.e. Hindi digits 'शू न् य, एक, दो, तीन चार, पाँ च, छः, सात, आठ, नौ' spoken by five males and five females repeated three times by each speaker, was created. Understanding the difference between acoustic features for different emotions is important for computer recognition and their classification. For the analysis of acoustic features which changes correspondingly with emotions were analyzed using PRATT speech processing software tool. Human perception experiment shows that the overall recognition of the emotions is about 60% for continuous sentences and 53% for isolated words. It is found that anger has highest intensity followed by neutral, happiness, surprise, sad and fear. Some differences were observed in case of continuous sentences. The dynamic changes in the pitch and intensity in these utterances have been analyzed.

This paper gives a comparison of two extracted features namely pitch and formants for emotion recognition from speech. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. The database used for recognition purpose was developed on Marathi language using 100 speakers. We have extracted features pitch and formants. Angry, stress, admiration, teasing and shocking have been recognized on the basis of features energy and formants. The classification technique used here is K-Nearest Neighbor (KNN). The result for formants was about 100% which is comparatively better than that of energy which was 80% of accuracy.

International Journal of Speech Technology, 2012

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.

2010

In this paper we present a comparative analysis of four classifiers for speech signal emotion recognition. Recognition was performed on emotional Berlin Database. This work focuses on speaker and utterance (phrase) dependent and independent framework. One hundred thirty three (133) sound/speech features have been extracted from Pitch, Mel Frequency Cepstral Coefficients, Energy and Formants. These features have been evaluated in order to create a set of 26 features, sufficient to discriminate between seven emotions in acted speech. Multilayer Percepton, Random Forest, Probabilistic Neural Networks and Support Vector Machine were used for the Emotion Classification at seven classes namely anger, happiness, anxiety/fear, sadness, boredom, disgust and neutral. In the speaker dependent framework, Probabilistic Neural Network reaches very high accuracy(94%), while in the speaker independent framework the classification rate of the Support Vector Machine reaches 80%. The results of numerical experiments are given and discussed in the paper.

2014

Speech emotion recognition (SER) has an increasingly significant role in the interactions among human beings, as well as between human beings and computers. Emotions are an inherent part of even rational decision making. The correct recognition of the emotional content of an utterance assumes the same level of significance as the proper understanding of the semantic content and is an essential element of professional success. Prevalent speech emotion recognition methods generally use a large number of features and considerable signal processing effort. On the other hand, this work presents an approach for SER using minimal features extracted from appropriate, sociolinguistically designed and developed emotional speech databases. Whereas most of the reported works in SER are based on acted speech with its exaggerated display of emotions, this work focuses on elicited emotional speech in which emotions are induced. Since female speech is more expressive of emotions, this research inve...