An Approach for Generating Pattern-Based Shorthand Using Speech-to-Text Conversion and Machine Learning

2013 ◽  
Vol 22 (3) ◽  
pp. 229-240
Author(s):  
K. R. Abhinand ◽  
H. K. Anasuya Devi
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Knowledge Representation ◽  
Natural Language ◽  
Unique Sequence ◽  
Speech Input ◽  
Text Input ◽  
Input Signals ◽  
New Knowledge ◽  
Textual Form

AbstractRapid handwriting, popularly known as shorthand, involves writing symbols and abbreviations in lieu of common words or phrases. This method increases the speed of transcription and is primarily used to record oral dictation. Someone skilled in shorthand will be able to write as fast as the dictation occurs, and these patterns are later transliterated into actual, natural language words. A new kind of rapid handwriting scheme is proposed, called the Pattern-Based Shorthand. A word on a keyboard involves pressing a unique sequence of keys in a particular order. This sequence forms a pattern that defines the word. Such a pattern forms the shorthand for that word. Speech recognition involves identifying, by a machine, the words spoken by a speaker. These spoken words form speech input signals to a computer that is equipped to correctly recognize the words and do further action, such as convert it to text. From this text input, unique shorthand patterns are generated by the system. The system employs machine learning to improve its performance with experience, by creating a dictionary of mappings from word to patterns in such a way that the access to existing patterns is faster with progression. This forms a new knowledge representation schema that reduces the redundancy in the storage of words and the length of information content. In conclusion, the speech is converted into textual form and then reconstructed into Pattern-Based Shorthand.

scholarly journals Towards implementing an AI chatbot platform for museums

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Savvas Varitimiadis ◽  
Konstantinos Kotis ◽  
Andreas Skamagis ◽  
Alexandros Tzortzakakis ◽  
George Tsekouras ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language ◽  
Language Processing ◽  
Significant Interaction ◽  
Knowledge Graph ◽  
Preliminary Design ◽  
Unique Role ◽  
Museum Visitors ◽  
Ongoing Work ◽  
New Knowledge

Recently, understanding their unique role in storytelling and aiming to attract more visitors, several museums have integrated modern ICT technologies. The problem with these technologies however is that gradually tend to be of no real interest to visitors, lack of significant interaction, cannot be continuously updated, and eventually distract visitors from experiencing the exhibits. Museum visitors do not need to be impressed by a technological application but need to learn about the stories of the exhibits in a creative, human-centered and interactive manner. This paper presents an ongoing work towards implementing a new interactive technological trend for museums, i.e., a museum chatbot platform, namely MuBot. The MuBot platform aims to provide museums the opportunity to create simple, interactive and human-friendly apps for their visitors. Such apps will integrate an intelligent chatbot that uses some of the most advanced AI technologies of Machine Learning, Natural Language Processing/Generation, and the Semantic Web. Museum visitors will be able to use a chatbot application that will be created through the MuBot platform, to chat with a ‘smart’ exhibit. They will be able to ask questions through text or voice (in natural language) and receive audible or written answers. The more the visitors ask, the more MuBot will learn and store new knowledge in its knowledge base. The paper presents a preliminary design of the proposed MuBot platform, experimenting with first prototype implementations using the well-known Dialogflow framework, as well as using a Knowledge Graph-based approach.

Drug repurposing against Parkinson's disease by text mining the scientific literature

2020 ◽  
Vol 38 (4) ◽  
pp. 741-750 ◽  
Author(s):  
Yongjun Zhu ◽  
Woojin Jung ◽  
Fei Wang ◽  
Chao Che
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Scientific Literature ◽  
Drug Repurposing ◽  
Content Type

PurposeDrug repurposing involves the identification of new applications for existing drugs. Owing to the enormous rise in the costs of pharmaceutical R&D, several pharmaceutical companies are leveraging repurposing strategies. Parkinson's disease is the second most common neurodegenerative disorder worldwide, affecting approximately 1–2 percent of the human population older than 65 years. This study proposes a literature-based drug repurposing strategy in Parkinson's disease.Design/methodology/approachThe literature-based drug repurposing strategy proposed herein combined natural language processing, network science and machine learning methods for analyzing unstructured text data and producing actional knowledge for drug repurposing. The approach comprised multiple computational components, including the extraction of biomedical entities and their relationships, knowledge graph construction, knowledge representation learning and machine learning-based prediction.FindingsThe proposed strategy was used to mine information pertaining to the mechanisms of disease treatment from known treatment relationships and predict drugs for repurposing against Parkinson's disease. The F1 score of the best-performing method was 0.97, indicating the effectiveness of the proposed approach. The study also presents experimental results obtained by combining the different components of the strategy.Originality/valueThe drug repurposing strategy proposed herein for Parkinson's disease is distinct from those existing in the literature in that the drug repurposing pipeline includes components of natural language processing, knowledge representation and machine learning for analyzing the scientific literature. The results of the study provide important and valuable information to researchers studying different aspects of Parkinson's disease.

scholarly journals An Examination of Emotion Recognition using Machine Learning Algorithms on Different Speech Databases

2020 ◽  
Vol 9 (4S2) ◽  
pp. 37-39
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Natural Language ◽  
Emotion Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Speech Recognition System ◽  
Mel Frequency Cepstral Coefficients ◽  
Natural Languages

The speech recognition system plays a vital role in understanding the emotions of natural language. The identification of emotions from speech is a challenging task. The performance of the speech recognition system is effects on the speech signals. The speech contains different emotions feelings. Many researchers introduced different emotion recognition techniques. However, these techniques achieved better performance but unsatisfied in identify emotion of natural languages. This paper proposed a novel speech recognition system, which identify the emotions based on the speech signals.The Mel Frequency Cepstral Coefficients (MFCC) features. On the resultant features of speech applied crossvalidation using the test emotions. The performance of the proposed system verify with the SVM and other two classifiers. The proposed emotion recognition system achieves better performance. The empirical results shows that the proposed system outperforms when compare with different classifiers and databases.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

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