Gender Classification from Speech (original) (raw)

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.

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.

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.

Multilanguage Speech-Based Gender Classification Using Time-Frequency Features and SVM Classifier

2021

Speech is the most significant communication mode among human beings and a potential method for humancomputer interaction (HCI). Being unparallel in complexity, the perception of human speech is very hard. The most crucial characteristic of speech is gender, and for the classification of gender often pitch is utilized. However, it is not a reliable method for gender classification as in numerous cases, the pitch of female and male is nearly similar. In this paper, we propose a time-frequency method for the classification of gender-based on the speech signal. Various techniques like framing, Fast Fourier Transform (FFT), auto-correlation, filtering, power calculations, speech frequency analysis, and feature extraction and formation are applied on speech samples. The classification is done based on features derived from the frequency and time domain processing using the Support Vector Machines (SVM) algorithm. SVM is trained on two speech databases Berlin Emo-DB and IITKGP-SEHSC, in which a total of 400 speech samples are evaluated. An accuracy of 83% and 81% for IITKGP-SEHSC and Berlin Emo-DB have been observed, respectively.

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.

Automatic identification of gender from speech

Speech Prosody 2016, 2016

Identifying the gender of a speaker from speech has a variety of applications ranging from speech analytics to personalizing human-machine interactions. While gender identification in previous work has explored the use of the statistical properties of the speaker's pitch features, in this paper, we explore the impact of using spectral features in conjunction with pitch features on identifying gender. We present a novel approach that leverages pitch feature trajectories in the interest of identifying the speaker's gender with as little speech as possible. We also investigate the cross-lingual robustness of a model trained on English speakers to identify the gender of German speakers. Finally, we present a model for gender detection in German that outperforms the state-of-the-art results on a benchmark data set.

Gender Identification Via Voice Analysis

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019

Human voice is basically sound which is made by humans from their vocal tracts. Voice is made of different constituents and has various characteristics such as frequency, amplitude etc. These characteristics are produced by combination of vocal folds and articulations. This paper reflects development of a system using these characteristics which altogether are called acoustic parameters to detect the gender of the speaker. We have used four models to classify the genders namely CART, XGBoost, SVM and Random Forest. An ensemble of all the models is also used to make the entire system more accurate. This system can be used as a building block for many other softwares where it will take the first step to extract the acoustic parameters and detect the gender of the speaker.