International Research Journal of Engineering and Technology (IRJET) Volume: 09 Issue: 11 | Nov 2022
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Text Summarization and Conversion of Speech to Text Prof. Priyanka Abhale1, Dawood Dalvi2, Sibin Alex3, Aman Jham4, Viraj Akte5 ALARD COLLEGE OF ENGINEERING & MANAGEMENT (ALARD Knowledge Park, Survey No. 50, Marunji, Near Rajiv Gandhi IT Park, Hinjewadi, Pune-411057) Approved by AICTE. Recognized by DTE. NAAC Accredited. Affiliated to SPPU (Pune University). ----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - This article describes of Recurrent neural
Segmentation solves this issue and makes the transcription easier to read.
networks, and deep learning algorithm fusion for Text Summarization System and analyzing the text learning process. After that, text analysis learning models are summarized. In addition, applications of deep learning-based text analysis are introduced. Speech is the most important part of communication between human beings. Though there are different means to express our thoughts and feeling, speech is considered as the main medium for communication. Speech recognition is the process of making a machine recognize the speech of different people based on certain words or phrases. End-to-end deep learning methods can be used to identify and simplify the spatial representation of text data and semantic information. This study examines deep learning-based text analysis
1.2 Normalization
Key Words: (Summarization, Speech, Text, Speech to Text, Audio, Words)
A crucial component of the field of data management is data cleaning. A database's whole contents are reviewed as part of the data cleansing process, and any information that is missing, inaccurate, duplicated, or irrelevant is either updated or removed. Data cleansing involves finding a technique to optimise the dataset's accuracy without necessarily messing with the existing data. It does not just involve removing the old information to make room for new data. The process of identifying and fixing incorrect data is known as data cleaning. The majority of tasks performed by organisations rely on data, but few do so in a way that is effective. The most crucial phase in the data process is cleaning, categorization, and standardisation of the data.
1. INTRODUCTION
1.3 Feature Extraction
Text Summarization helps us to give a summary report of the given Paraphrase. Variations in the pronunciation are quite evident in each individual’s speech. Even though speech is the easiest way of communication, there exist some problems with speech recognition like the fluency, pronunciation, broken words, stuttering issues etc. All these have to be addressed while processing a speech. Lengthy documents are difficult to read and understand as it consumes lot of time. Text summarisation solves this problem by providing a shortened summary of it with semantics.
Reducing the amount of resources needed to describe a huge quantity of data is the goal of feature extraction. One of the main issues with analyzing complex data is the sheer amount of variables that are involved. Results can be improved utilizing constructed sets of application-dependent features, often built by an expert. Analysis with a large number of variables normally demands a substantial amount of memory and computer capacity. The technique of feature engineering is one of these.
1.1 Segmentation
Modeling entails teaching an algorithm for machine learning to predict labels from features, tweaking it for the needs of the business, and validating it. A computer model learns to carry out classification tasks directly from text or voice using deep learning. Natural language processing (NLP) uses the text summarizing technique to provide a succinct and accurate summary of a reference document. It is exceedingly challenging to manually summarize a lengthy article. Machine learning-based text summarization is still an extensive research area.
1.4 Modelling
The task of splitting text into meaningful segments is called text segmentation. Words, sentences, or topics can make up these segments. Topic segmentation, a type of Text Segmentation task that breaks up a lengthy text into sections that correspond to distinct topics or subtopics, is the subject of some of our examination. Take, for instance, an automated transcription of an hourlong podcast. It can be easy to lose track of which sentence you are reading because the transcription can be long. By dividing the text into multiple segments, Automatic Topic
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The statistical method of modelling is used to identify hidden themes or keywords in a group of papers. A probabilistic approach for learning, analyzing, and finding topics from the
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