Recognizing human emotions through vocal channel has gained increased attention recently. In this paper, we study how used features, and classifiers impact recognition accuracy of emotions present in speech. Four emotional states are considered for classification of emotions from speech in this work. For this aim, features are extracted from audio characteristics of emotional speech using Linea...

Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching...

Vocal and nonvocal segmentation is an important task in singing voice signal processing. Before identifying the singer it is necessary to locate the singer’s voice in a song. Maximum of the songs start with a piece of instrumental accompaniment known as ‘prelude’ in musical terms after which the singing voice comes into play. Therefore, it is necessary to detect the vocal region in the song in ...

This paper presents the Automatic Genre Classification of Indian Tamil Music andWestern Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted fro...

This work describes the techniques used for spoofed speech detection for the ASVspoof 2017 challenge. The main focus of this work is on exploiting the differences in the speech-specific nature of genuine speech signals and spoofed speech signals generated by replay attacks. This is achieved using glottal closure instants, epoch strength, and the peak to side lobe ratio of the Hilbert envelope o...

In the presence of noise and sensor mismatch condition performance of a conventional automatic Hindi speech recognizer starts to degrade, while we human being are able to segregate, focus and recognize the target speech. In this paper, we have used auditory based feature extraction procedure Gammatone frequency cepstral coefficient (GFCC) for Hindi phoneme classification. To distinguish vowels ...

In this paper, we present new dynamic features derived from the modulation spectrum of the cepstral trajectories of the speech signal. Cepstral trajectories are projected over the basis of sines and cosines yielding the cepstral modulation frequency response of the speech signal. We show that the different sines and cosines basis vectors select different modulation frequencies, whereas, the fre...

Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts YouTube. Some of methods in study are pitch shifting for augmentation, audio data, basic statistics taking mean, median, min, max, standard deviation e...

The aim of this work is to increase intelligibility of HMMbased synthetic speech in noisy environments by modifying clean synthetic speech given that noise is known. For that purpose we need a measure for intelligibility of speech in noise that can automatically define the sort of modifications that we need to apply. In previous experiments [1] we have observed that spectrum envelope modificati...