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

Abstract

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.

Research on English speaking assessment algorithms based on deep learning

“…In language recognition work, the N-Gram model is commonly used to accomplish this task. In this model, the occurrence of the Nth word is influenced by only the previous N-1 words, and the product of the occurrence probability of each word is the probability of the whole sentence [9]. Currently, RNNLM, which is an RNN-based language model, is gradually introduced in speech recognition, and it enables the modeling of longer historical information.…”