An Exploratory Study of the Effects of Using Speech Recognition to Write

Melcher, Joseph M.

doi:10.1037/e416902005-255

Cited by 1 publication

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“…The number of groups identified for the study were two with the sample size used for experimenting speech to text recognition using English Corpora is 62 and 62 with 156 samples as existing and proposed two groups. (Melcher 2003;Tabossi, Scott, and Burani 1991). The computation is performed using G-power 0.8 with alpha value is 0.05 and beta value is 0.2 with a confidence interval at 95%.…”

Section: Methodsmentioning

confidence: 99%

Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords

Prasad¹,

Deepa²

2021

revistageintec

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Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies speech recognition that can be compared with convolutional is the second technique. Results and Discussion: RNN-LSTM from the dataset speech recognition, feature Telugu_id produce accuracy 93% and precision 68.04% which can be comparatively higher than CNN accuracy 66.11%, precision 61.90%. It shows a statistical significance as 0.007 from Independent Sample T-test. Conclusion: The RNN-LSTM performs better in finding accuracy and precision when compared to CNN.

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Section: Methodsmentioning

confidence: 99%