KNN based emotion recognition system for isolated Marathi speech

International Journal of Computer Applications, 2014

The research work describes the procedure and the development of Isolated Marathi Emotional Speech Database. The database consists of samples, collected from 50 speakers including males and females who simulated the emotions producing by the Marathi utterances which are used in everyday communication and are interpretable in all applied emotions. The speech samples were enhanced by spectral subtraction method and distinguished by the various real life situations. The recorded speech samples were categorized in three basic categories i.e. Happy, Sad and Angry.

IJRET, 2013

Speech processing is the study of speech signals, and the methods used to process them. In application such as speech coding, speech synthesis, speech recognition and speaker recognition technology, speech processing is employed. In speech classification, the computation of prosody effects from speech signals plays a major role. In emotional speech signals pitch and frequency is a most important parameters. Normally, the pitch value of sad and happy speech signals has a great difference and the frequency value of happy is higher than sad speech. But, in some cases the frequency of happy speech is nearly similar to sad speech or frequency of sad speech is similar to happy speech. In such situation, it is difficult to recognize the exact speech signal. To reduce such drawbacks, in this paper we propose a Telugu speech emotion classification system with three features like Energy Entropy, Short Time Energy, Zero Crossing Rate and K-NN classifier for the classification. Features are extracted from the speech signals and given to the K-NN. The implementation result shows the effectiveness of proposed speech emotion classification system in classifying the Telugu speech signals based on their prosody effects. The performance of the proposed speech emotion classification system is evaluated by conducting cross validation on the Telugu speech database

In machine interaction with human being is yet challenging task that machine should be able to identify and react to human non-verbal communication such as emotions which makes the human computer interaction become more natural. In present research area automatic emotion recognition using speech is an essential task which paid close attention. Speech signal is a rich source of information and it is an attractive and efficient medium due to its numerous features of expressing approach & extracting emotions through speech is possible. In this paper emotions is recognized through speech using spectral features such as Mel frequency cepstrum coefficient prosodic features like pitch , energy and were utilized & study is carried out using K-Nearest Neighbor classifiers and Gaussian mixture model classifier which is used for detection of six basic emotional states of speaker's such as anger ,happiness , sadness , fear , disgust and neutral using Berlin emotional speech database.

2015

Many attempts have been made for developing a system which can recognize emotions from speech signals effectively. The research shows that various features namely prosodic and spectral have been used for emotion recognition from speech. This paper is one search approach towards same. The database used for recognition purpose was developed on Marathi language using 100 speakers. Here we have extracted the prosodic features i.e. energy and formants from speech signals. We have also applied linier discriminates analysis as the classification technique. Further we have compare the results gain on the basis of extracted features and came to know that formant gives quit good results compared to that of energy. KeywordsDatabase, Emotion Recognition, Formants, Feature extraction, Linier discriminant Analysis, Speech.

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, 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

In machine interaction with human being is yet challenging task that machine should be able to identify and react to human non-verbal communication such as emotions which makes the human computer interaction become more natural. In present research area automatic emotion recognition using speech is an essential task which paid close attention. Speech signal is a rich source of information and it is an attractive and efficient medium due to its numerous features of expressing approach & extracting emotions through speech is possible. In this paper emotions is recognized through speech using spectral features such as Mel frequency cepstrum coefficient prosodic features like pitch , energy and were utilized & study is carried out using K-Nearest Neighbor classifiers , Support Vector Machine Classifier and Gaussian mixture model classifier which is used for detection of six basic emotional states of speaker's such as anger ,happiness , sadness , fear , disgust and neutral using Berlin emotional speech database.

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.