Affiliation:
1. SIPNA College of Engineering and Technology, Amravati, Maharashtra, India
Abstract
"Jarvis" was the protagonist of Tony's Stark starring in the movie Iron Man. Unlike the first jokes when Jarvis was Stark's servant, Jarvis' movie version is a smart computer that communicates critical issues, monitors his family, and helps build and organize his hero's suit. In this project, Jarvis is a Digital Health Assistant who uses social media i.e., voice that creates two-way communication between people and his personnel Computer, performing various tasks such as calling and chatting on WhatsApp, taking notes, locating, etc. In our project, we use the word just as communication means that Jarvis is a Speech recognition program. The concept of speech technology combines two technologies: Synthesizer and vision. Speech synthesizer captures input and generates audio streams as output this is achieved using the pyttsx3 library accompanied by Speech function. Speech recognition. On the other hand, he did the opposite. It treats audio streaming as input and thus converts it into text writing and this output uses the Speech Recognition library accompanied by the TakeCommand function. The word is a symbol of endless knowledge. Direct analysis and integration of complex voice signal is due to the vast amount of information contained in the signal. Therefore, digital signal processes such as Feature Visibility and Feature Matching are introduced to represent the voice signal. For this project, we are directly using a speech engine that uses an output element such as Mel scaled frequency cepstral. The mel scale frequency cepstral coefficients (MFCCs) obtained from the fourier transform and filter bank analysis are probably the most widely used in high-level speech recognition systems. We aim to create more jobs that can help people in their daily lives and reduce their efforts as in this project we have introduced Chrome and Youtube Automation. In our test, we checked that all of this functionality works well. We test this on 2 speakers (1 female and 1 male) to find the exact motive for the speculation.
Reference13 articles.
1. https://docs.python.org/3/
2. Ashish Jain, Hohn Harris, Speaker identification using MFCC and HMM based techniques, university Of Florida, April 25,2004.
3. http://www.microsoft.com/MSDN/speech.html, downloaded on 2Oct 2012.
4. Young Steve, A Review of Large-vocabulary Continuous-speech Recognition, IEEE SP Magazine, 13:45- 57, 1996, ISSN 1053-5888.
5. https://www.udemy.com/?deal_code=&utm_term=Homepage&utm_content=Textlink&utm_campaign=Rakuten-default&ranMID=39197&ranEAID=%2F68Yt01SgtI&ranSiteID=_68Yt01SgtI-VvNIQiohSWcLJRXuQasY bg&LSNPUBID=%2F68Yt01SgtI&utm_source=aff-campaign&utm_medium=udemyads