Researchers found out that there are little to no studies about Closed Captioning involving the Tagalog Language. For the latest researches found by the researchers, the system performance rate, in terms of accuracy, ranges from 81% to 87% within the word bank of 35 words using the Tagalog language. Because of this, the system lacks the vocabulary to create sentences that can be used in documentaries, news, etc. The researchers decided to create a system with a higher accuracy rate and increased vocabulary implemented without the compromising of speed in speech to text system. This is made possible by convolutional neural network (CNN) in Python Language that uses Low Pass Filter, Librosa MFCC Feature Extraction and Keras Modeling. The system yielded an accuracy rating of 66.17% for male speakers, 81.64% for female speakers, 38.43% for accuracy with background noise, and 54.14% for 1kHz monotone. The speed of the system averaged at 1.47 seconds and with the vocabulary having 100% recognition rate. The Filipino Speech to Text system uses convolutional neural network in both its training and testing phase in which audio files are converted into 3d models that is used to recognize a word.

Filipino Speech to Text System using Convolutional Neural Network

An inevitable factor that makes the concept of electronic voting irresistible is the fact that it offers the possibility of exceeding the manual voting process in terms of convenience, widespread participation, and consideration for People Living with Disabilities. The underlying voting technology and ballot design can determine the credibility of election results, influence how voters felt about their ability to exercise their right to vote, and their willingness to accept the legitimacy of electoral results. However, the adoption of e-voting systems has unveiled a new set of problems such as security threats, trust, and reliability of voting systems and the electoral process itself. This paper presents a critical literature review on concepts, architectures, and existing authentication strategies in voice recognition systems for the e-voting system for the disabled electorate. Consequently, in this paper, an intelligent yet secure scheme for electronic voting systems specifically for people living with disabilities is presented.

/pdf/voice-recognition-systems-for-the-disabled-electorate-1snwbqor.pdf

Voice Recognition Systems for The Disabled Electorate: Critical Review on Architectures and Authentication Strategies

Deaf individuals face a lot of inconvenience in communicating due to various type of hearing loss, and their hearing loss may affect their interpersonal communication and ability to recognize facial emotions. Little research has been done on how new technology could assist and solve hearing impairments of deaf people. This paper examines the methods created by numerous researchers to convert speech to text in real time, which would help deaf individuals communicate. We examine these approaches and list their benefits and drawbacks in order to see where this domain might be improved. This publication intends to provide young scholars interested in contributing to this topic with an overview of the numerous options available.

Real Time Speech to Text Using Automatic Speech Recognition

In this work, we evaluate the performance of objective measures of noisy input of continuous speech signal through the Hybrid method using Voice Activity Detection (VAD) and Speech Enhancement Algorithm (SEA). Automatic Speech Recognition (ASR) is an important technology, which enables natural human-machine interaction, for over five decades. The objective of this work consists in working out an identification of continuous speech recognition. The methodology presented allows evaluating the process which includes a speech-to-text system using continuous word recognition with a vocabulary of ten words (digits 0 to 9). In the training period, the continuous digits are recorded using 8-bit Pulse Code Modulation (PCM) with a sampling rate of 8 KHz and save as a wave format file using sound recorder software. For a given word in the vocabulary, the system builds an Hidden Markov Model (HMM) model and trains the model during the training phase. The training steps, from VAD, Speech Enhancement to HMM model building, are performed using PC-based Matlab programs. An overall Recognition Accuracy (RA) of 72.45% is achieved from the proposed speech recognition system working under different environment condition for an uttered word.

Performance analysis of hybrid model of robust automatic continuous speech recognition system

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Performance analysis of hybrid model of robust automatic continuous speech recognition system