# Translating Speech to Indian Sign Language Using Natural Language Processing
## 7 Methodology  
Figure 6 shows the natural language model's architecture. If the text input which is given as input to the model matches a video in the database, then the input is given as the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**State**} & \multicolumn{3}{c}{**Next State**} \\ \cline{2-4}  & **Verb** & **Noun** & **Adj** \\ \hline \multirow{3}{*}{Previous (current) state} & Verb & P(verb\(\,\)I verb) = 0.3 & P(Noun\(\,\)I verb) = 0.96 & P(Adj\(\,\)I verb) = 0.01 \\ \cline{2-4}  & Noun & P(verb\(\,\)I Noun) = 0.45 & P(Noun\(\,\)I Noun) = 0.05 & P(Adj\(\,\)I Noun) = 0.5 \\ \cline{2-4}  & Adj & P(verb\(\,\)I Adj) = 0.02 & P(Noun\(\,\)I Adj) = 0.95 & P(Adj\(\,\)I Adj) = 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Transition Matrix (Probability of Transition). Figure 5: Markov Model with three states. keyword; otherwise, the input text is processed through the various NLP technologies and then the keywords are generated. Each part of the technology is discussed in more detail in the heading \"Methodology\". NLTK is the heart of the audio to Indian Sign Language conversion system, as it is the most powerful open-source NLP library which is used to assist with human language data. Text processing is performed using NLTK, which involves various steps, such as tokenization, the removal of stop words, lemmatization, parse tree generation, part-of-speech (POS) tagging, etc. * **Tokenization**  
Tokenization is the process of splitting text into a list of words also known as tokens. NLTK has a module named tokenize () which is further divided into two types, i.e, word tokenize to split a sentence into a sequence of words and sentence tokenize to split a paragraph into a list of sentences. * **The removal of stop words**  
Stop words are a list of very common but less informative words that can be ignored. For example--her, me, has, itself, he, so, too, they, them, etc. Since they are not so important in the sentence, they can be removed from the sentence. This will improve the overall performance of the system. * **Parsing**  
Parsing is the syntax analysis phase in which it checks whether the string obtained after tokenization belongs to proper grammar or not. Parsing helps to adjust the text  
Figure 6: Natural Language Model Architecture. based on the target language's grammar structure. One of the most used parsers is the Stanford Parser. * **Lemmatization** The process of transforming the inflected forms of a word to its root-based dictionary form is known as lemmatization. This root-based dictionary form of a word is referred to as a lemma. The importance of this step lies in ISL, as it requires a root word. To check the results of lemmatization, we analyze its results through sample sentences. For example--\"He was playing and eating at the same time\". The results in Table 5 show that lemmatization alone is not sufficient enough to give accurate root words, as it does not take into consideration the context of the sentence. To overcome this problem, part-of-speech tagging comes into the picture. * **Part-of-speech tagging** POS tagging refers to the process of labeling words with different constructs of English grammar, such as adverbs, adjectives, nouns, verbs, prepositions, etc. POS is a collection of a list of tuples where the first part of the tuple is the word itself and the second part is a tag that identifies whether the word is an adjective, verb, noun, etc. To check whether part-of-speech tagging can improve the results obtained after lemmatization, we analyze the same sample sentence used above in lemmatization. The results in Table 6 show that after integrating part-of-speech tagging with lemmatization it gives a correct base form of a word, which in turn improves the accuracy of word to base word conversion. Thus, the combination of part-of-speech tagging and lemmatization is used in our proposed system to enhance the accuracy of our system.