A New Offline Persian Hand Writer Recognition based on 2D-Wavelet Transforms

Abstract

Abstract-Speech Recognition Technology can be embedded in various real time applications in order to increase the human-computer interaction. From robotics to health care and aerospace, from interactive voice response systems to mobile telephony and telematics, speech recognition technology have enhanced the humanmachine interaction. Gender recognition is an important component for the application embedding speech recognition as it reduces the computational complexity for the further processing in these applications. The paper involves the extraction of one of the most dominant and most researched up on speech feature, Mel coefficients and its first and second order derivatives. We extracted 13 values for each of these from a data-set 46 speech samples containing the Hindi vowels (आ, इ, ई, उ, ऊ, ऋ, ए, ऎ, ऒ, ऑ) and trained them using a combined model of SVM and neural network classification to determine their gender using stacking. The results obtained showed the accuracy of 93.48% after taking into consideration the first Mel coefficient. The purpose of this study was to extract the correct features and to compare the performance based on first Mel coefficient.

Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN

“…Our modern life is fully dependent on technology. As many technologies are growing rapidly like machine learning, deep learning, big data, and various algorithms are improving day by day (Alsulaiman et al, 2011;Chaudhary & Sharma, 2018;Keyvanrad & Homayounpour, 2010;Livieris et al, 2019;Pahwa & Aggarwal, 2016), we can see the development of machine learning for research work as well as its popularity is become high to apply in many fields. As machine learning is a subgroup of artificial intelligence, it learns from experience or data by developing an algorithm to teach a computer system to make decisions on various problems like speech recognition, image processing, etc.…”

“…To implement the system cepstral peak prominence, spectral harmonic magnitude, harmonic-to-noise ratio are used as features. One of the most dominant and most researched features Mel coefficients and its first and second derivatives (Pahwa & Aggarwal, 2016) used to determine the gender from the 46 speech sample dataset containing the Hindi vowels. Moreover, the proposed method is capable of working well with vowels, although the researchers didn't mention anything about the model that it can perform well or bad for larger sentences.…”

“…Mel Frequency Cepstral Coefficient (MFCC): MFCC is a representation of a short-term power spectrum of sound which is extracted from the speech signal to perform the speech recognition tasks. It is one of the most popular features in the speech recognition system (Pahwa & Aggarwal, 2016;Practical Cryptography, n.d.). The MFCC feature extraction method is based on many steps.…”

“…Gender recognition is a technique to determine gender categories by the speaker's voice signal analysis (Keyvanrad & Homayounpour, 2010). As gender recognition is a significant task, it increases the performance of applications such as human-computer interaction, emotion recognition, person identification, sorting of telephone calls by gender categorization, online advertisements involving voice (Alsulaiman et al, 2011;Chaudhary & Sharma, 2018;Pahwa & Aggarwal, 2016). Moreover, Human voices have different accents due to differences in culture, history, and geographical locations.…”

A Dataset for Speech Recognition to Support Arabic Phoneme Pronunciation

“…[18] Scheme was evaluated using a speaker independent Arabic database used in HTK, error detection achieves the overall correct rate of speech recognition ranges from 72.73% to 98.19% from different stages and conditions of the system. MFCC also was used by [20] as a feature extraction technique reduced by using Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) for speech recognition. The cognitive system contains a cognitive content represented in an attractive multi-media program which is supported by pictures, songs, practices, and some stories.…”