Multilanguage Speech-Based Gender Classification Using Time-Frequency Features and SVM Classifier (original) (raw)

Classification of Gender Based On Voice Using Support Vector Machine

Biometric authentication is an emerging technology that utilize biometric data for the purpose of person identification or recognition in security applications. The speech recognition problem can be modeled as a classification problem, where one wants to get the best degree of separability between classes representing the voice. Gender identification refers to the classical problem of distinguishing active male speech from female speech.Many approaches have been devised to improve the performance of gender identification with relevant strategies. An efficient method comparing Support Vector Machine and Artificial Neural Network methods are implemented here. The MFCC features are extracted and are given to SVM for different kernels and also to ANN and their performance is evaluated.

A Hybrid Approach to Gender Classification using Speech Signal

International Journal of Scientific Research in Science, Engineering and Technology

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

Speech-to-Gender Recognition Based on Machine Learning Algorithms

International Journal of Applied Mathematics Electronics and Computers

Speech recognition has several application areas such as human machine interaction, classification of phone calls by gender, voice tagging, STT, etc. Predicting gender from audio signals is a problem that is easy for humans to solve, difficult to solve by a computer. In this study, a model based on MFCC and classification with machine learning is proposed for gender estimation from Turkish voice signals. Within the scope of the study, 58 different series and films were examined and a new original dataset was created with 894 audio recordings consisting of 5 sec sections taken from them. Mel-frequency cepstral coefficients (MFCC) and spectrogram, which are frequently used in the literature, were used for feature extraction from audio data. The results were first evaluated separately using two features in one way. A hybrid feature vector was then created using two feature vectors. Different machine learning algorithms (LR, DT, RF, XGB etc.) were tested in the classification process an...

Gender Classification by Speech Analysis

2017

Gender classification is widely used in automatic speech recognition systems to recognize a speaker speaking continuously in any language. This work aims at analysing speech signals based on some parameters so as to predict the gender of the speaker. This paper comprises of male and female voice samples which were collected to form a database. Parameters such as mean, variance and standard deviation were determined to help in classifying the gender of the speaker.

Voice Based Gender Classification Using Machine Learning

Gender identification is one of the major problem speech analysis today. Discovering the gender from acoustic data i.e., pitch, median. Frequency etc. Machine learning gives ominous results for classification problem in all the research domains. There are several performance metrics to assess algorithms of an area. Aim is to identify gender, with five different algorithms: Linear Discriminant Analysis, K-Nearest Neighbor, Characterization and Regression Trees, Random Forest, and Support Vector Machine on premise of eight unique techniques. The main parameter in assessing any algorithms is its performance. Misclassification rate must be less than in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are using the different ML algorithms and find the best one for gender classification of acoustic data.

Gender Classification from Speech

This project uses MATLAB to devise a gender classifier from speech by analyzing the voice samples containing an arbitrary sentence. The speech signal is assumed to contain only 1 speaker, speaking in English, with no other background sounds. The classifier analyses the voice samples by using a pitch detection algorithm based on computing the short-time autocorrelation function of the speech signal.

Gender recognition by speech analysis

Gender Voice recognition is actually a very important branch of research in the field of acoustics and speech processing. The human voice exhibits very interesting aspects which have been analyzed and discussed in this paper. This paper is about an investigation on speech signals to devise a gender classifier. Gender classification by speech analysis basically aims to predict the gender of the speaker by analyzing different parameters of the voice sample. A database consisting of 200 voice samples of celebrities, both male and female, using an open source tool was created. The short-time autocorrelation and average magnitude difference function (AMDF) was used collectively by assigning some weightage factor and a threshold is set on the basis of the fact that male has lower fundamental frequency(nearly 120Hz) as compared to female(nearly 200Hz). It is a text and language independent analysis Moreover audacity tool is used for noise suppression purpose.

Gender Identification Using A Speech Dependent Voice Frequency Estimation System

The objective of this research paper is to design a speaker dependent system that determines the gender of the speaker using the pitch of the speaker's voice. A speaker dependent system is a system which can recognize speech from one particular speaker. The pitch of the speaker's voice is estimated by applying various pitch estimation techniques, namely FFT, Cepstral Analysis, Autocorrelation and MFCC to correctly determine the gender of a speaker by classifying the pitch of the speaker's voice based on existing frequency values that were obtained using above techniques. The proposed system can be used in implementations of AI technologies, Internet of Things and various other future applications to detect the gender of the speaker with maximum possible efficiency and accuracy. I Introduction For human beings, speech has proven to be the major modicum of communication with other humans, animals and even machines. Every day, research is being undertaken by experts of various sciences across the world in the field of speech processing to study the characteristics of the speaker. Of all the characteristics, gender is the most fundamental and obvious one. There are a lot of possible applications where the gender of the speaker plays an important role. For example, an AI system that controls home appliances by receiving voice input from the user can modify its working based on the needs that arise from the gender of the user. To determine the gender, we use the pitch of the speaker's voice as a classifier. Lawrence R. And Michael J. Cheng et al(1976) defined a pitch detector as an important component of various speech processing systems, such as vocoder systems. A comparative study of various pitch detection algorithms they performed shows that accurate and reliable measurement of pitch from frequency waveform is difficult. They classified pitch detection algorithms into three broad categories: 1) Using time-domain properties of speech 2) Using frequency-domain properties of speech 3) Using both time and frequency domains The performance of the pitch detection algorithm is evaluated based on its speed, suitability, complexity and cost of implementation.

Gender classification using pitch and formants

Proceedings of the 2011 International Conference on Communication, Computing & Security - ICCCS '11, 2011

A gender classification system is proposed based on pitch, formants and combination of both. Ten Hindi digits database has been prepared for fifty speakers. Each Speaker has spoken each digit ten times. Formants derived from speech samples have been used for gender classification. Gender classification has been also done by using pitch extracted from different methods. Autocorrelation, Cepstrum and Average Magnitude Difference (AMDF) methods have been used for pitch determination from speech samples. Formants in combination with pitch are also used for gender classification. A feature vector consisting of pitches derived from all the above mentioned pitch determination methods was also used for gender classification. Experiments were performed for both open-set and closed-set gender classification. Autocorrelation method performed best for gender classification in open-set. Hybrid method (Autocorrelation +AMDF+ Cepstrum) performed best for gender classification in closed-set.