We found 6 results that contain "speech"

Posted on: #iteachmsu
Assessing Learning
Monday, Jan 11, 2021
EXAMPLES OF ARTIFICIAL INTELLIGENCE IN USE TODAY
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning, and planning.
Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it’s not the future – it is the present.

With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson, or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.

The following statistics will give you an idea of growth!

– In 2014, more than $300 million was invested in AI startups, showing an increase of 300%, compared to the previous year (Bloomberg)

– By 2018, 6 billion connected devices will proactively ask for support. (Gartner)

– By the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels and partners (Gartner)



Posted by: Rupali Jagtap
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Posted on 1: #iteachmsu
EXAMPLES OF ARTIFICIAL INTELLIGENCE IN USE TODAY
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning, and planning.
Today, Artificial Intelligence is a very popular subject that is widely discussed in the technology and business circles. Many experts and industry analysts argue that AI or machine learning is the future – but if we look around, we are convinced that it’s not the future – it is the present.

With the advancement in technology, we are already connected to AI in one way or the other – whether it is Siri, Watson, or Alexa. Yes, the technology is in its initial phase and more and more companies are investing resources in machine learning, indicating a robust growth in AI products and apps in the near future.

The following statistics will give you an idea of growth!

– In 2014, more than $300 million was invested in AI startups, showing an increase of 300%, compared to the previous year (Bloomberg)

– By 2018, 6 billion connected devices will proactively ask for support. (Gartner)

– By the end of 2018, “customer digital assistants” will recognize customers by face and voice across channels and partners (Gartner)



ASSESSING LEARNING
Posted by: Rupali Jagtap
Monday, Jan 11, 2021
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Posted on: #iteachmsu
Assessing Learning
Wednesday, Jan 27, 2021
Speech recognition
https://www.google.com/search?rlz=1C5GCEA_enLK882LK883&ei=5S7rX7WrN8jw9QOh-4KYAQ&q=chemical+tests&oq=chemical+tests&gs_lcp=CgZwc3ktYWIQAzIFCAAQyQMyAggAMgIIADICCAAyAggAMgIIADICCAAyAggAMgIIADICCAA6CAgAEMkDEJECOgUIABCRAjoKCAAQsQMQgwEQQzoICAAQsQMQgwE6BQgAELEDOgIILjoECAAQQzoOCC4QsQMQgwEQxwEQrwE6BwguELEDEEM6BQguELEDOgoIABCxAxDJAxBDOgcIABCxAxBDOggILhDHARCvAToGCAAQFhAeOggIABAWEAoQHlDs-esXWIea7BdgrZvsF2gCcAF4AYABoQKIAeARkgEGMC4xMy4ymAEAoAEBqgEHZ3dzLXdperABAMABAQ&sclient=psy-ab&ved=0ahUKEwj1se7ipfPtAhVIeH0KHaG9ABMQ4dUDCA0&uact=5
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition, or speech to text (STT).
Posted by: Rupali Jagtap
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Posted on 1: #iteachmsu
Speech recognition
https://www.google.com/search?rlz=1C5GCEA_enLK882LK883&ei=5S7rX7WrN8jw9QOh-4KYAQ&q=chemical+tests&oq=chemical+tests&gs_lcp=CgZwc3ktYWIQAzIFCAAQyQMyAggAMgIIADICCAAyAggAMgIIADICCAAyAggAMgIIADICCAA6CAgAEMkDEJECOgUIABCRAjoKCAAQsQMQgwEQQzoICAAQsQMQgwE6BQgAELEDOgIILjoECAAQQzoOCC4QsQMQgwEQxwEQrwE6BwguELEDEEM6BQguELEDOgoIABCxAxDJAxBDOgcIABCxAxBDOggILhDHARCvAToGCAAQFhAeOggIABAWEAoQHlDs-esXWIea7BdgrZvsF2gCcAF4AYABoQKIAeARkgEGMC4xMy4ymAEAoAEBqgEHZ3dzLXdperABAMABAQ&sclient=psy-ab&ved=0ahUKEwj1se7ipfPtAhVIeH0KHaG9ABMQ4dUDCA0&uact=5
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition, or speech to text (STT).
ASSESSING LEARNING
Posted by: Rupali Jagtap
Wednesday, Jan 27, 2021
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Posted on: #iteachmsu
Incorporating Technologies
Wednesday, Jul 21, 2021
Artificial Intelligence
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Posted by: Rupali Jagtap
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Posted on 1: #iteachmsu
Artificial Intelligence
The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
INCORPORATING TECHNOLOGIES
Posted by: Rupali Jagtap
Wednesday, Jul 21, 2021
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Posted on: #iteachmsu
Wednesday, Dec 6, 2023
NLP tasks
Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure—these just a few of the irregularities of human language that take humans years to learn, https://byjus.com/biology/flower/ but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful.
https://byjus.com/biology/flower/ https://byjus.com/biology/flower/

Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting. Some of these tasks include the following:

Speech recognition, also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
Part of speech tagging, also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
Named entity recognition, or NEM, identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
Co-reference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (e.g., ‘she’ = ‘Mary’), but it can also involve identifying a metaphor or an idiom in the text (e.g., an instance in which 'bear' isn't an animal but a large hairy person).
Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
Natural language generation is sometimes described as the opposite of speech recognition or speech-to-text; it's the task of putting structured information into human language.
Authored by: Super Admin - R
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Posted on 1: #iteachmsu
NLP tasks
Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, variations in sentence structure—these just a few of the irregularities of human language that take humans years to learn, https://byjus.com/biology/flower/ but that programmers must teach natural language-driven applications to recognize and understand accurately from the start, if those applications are going to be useful.
https://byjus.com/biology/flower/ https://byjus.com/biology/flower/

Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it's ingesting. Some of these tasks include the following:

Speech recognition, also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar.
Part of speech tagging, also called grammatical tagging, is the process of determining the part of speech of a particular word or piece of text based on its use and context. Part of speech identifies ‘make’ as a verb in ‘I can make a paper plane,’ and as a noun in ‘What make of car do you own?’
Word sense disambiguation is the selection of the meaning of a word with multiple meanings through a process of semantic analysis that determine the word that makes the most sense in the given context. For example, word sense disambiguation helps distinguish the meaning of the verb 'make' in ‘make the grade’ (achieve) vs. ‘make a bet’ (place).
Named entity recognition, or NEM, identifies words or phrases as useful entities. NEM identifies ‘Kentucky’ as a location or ‘Fred’ as a man's name.
Co-reference resolution is the task of identifying if and when two words refer to the same entity. The most common example is determining the person or object to which a certain pronoun refers (e.g., ‘she’ = ‘Mary’), but it can also involve identifying a metaphor or an idiom in the text (e.g., an instance in which 'bear' isn't an animal but a large hairy person).
Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
Natural language generation is sometimes described as the opposite of speech recognition or speech-to-text; it's the task of putting structured information into human language.
Authored by: Super Admin - R
Wednesday, Dec 6, 2023
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Posted on: #iteachmsu
Monday, May 6, 2024
Web Content Accessibility Guidelines (WCAG) 2.2
Web Content Accessibility Guidelines (WCAG) 2.2 defines how to make Web content more accessible to people with disabilities. Accessibility involves a wide range of disabilities, including visual, auditory, physical, speech, cognitive, language, learning, and neurological disabilities. Although these guidelines cover a wide range of issues, they are not able to address the needs of people with all types, degrees, and combinations of disability. These guidelines also make Web content more usable by older individuals with changing abilities due to aging and often improve usability for users in general.

WCAG 2.2 is developed through the W3C process in cooperation with individuals and organizations around the world, with a goal of providing a shared standard for Web content accessibility that meets the needs of individuals, organizations, and governments internationally. WCAG 2.2 builds on WCAG 2.0 [WCAG20] and WCAG 2.1 [WCAG21], which in turn built on WCAG 1.0 [WAI-WEBCONTENT] and is designed to apply broadly to different Web technologies now and in the future, and to be testable with a combination of automated testing and human evaluation. For an introduction to WCAG, see the Web Content Accessibility Guidelines (WCAG) Overview.

Significant challenges were encountered in defining additional criteria to address cognitive, language, and learning disabilities, including a short timeline for development as well as challenges in reaching consensus on testability, implementability, and international considerations of proposals. Work will carry on in this area in future versions of WCAG. We encourage authors to refer to our supplemental guidance on improving inclusion for people with disabilities, including learning and cognitive disabilities, people with low-vision, and more.

Web accessibility depends not only on accessible content but also on accessible Web browsers and other user agents. Authoring tools also have an important role in Web accessibility. For an overview of how these components of Web development and interaction work together, see:

Essential Components of Web Accessibility
User Agent Accessibility Guidelines (UAAG) Overview
Authoring Tool Accessibility Guidelines (ATAG) Overview
Where this document refers to WCAG 2 it is intended to mean any and all versions of WCAG that start with 2.
Authored by: Vijaya
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Posted on 1: #iteachmsu
Web Content Accessibility Guidelines (WCAG) 2.2
Web Content Accessibility Guidelines (WCAG) 2.2 defines how to make Web content more accessible to people with disabilities. Accessibility involves a wide range of disabilities, including visual, auditory, physical, speech, cognitive, language, learning, and neurological disabilities. Although these guidelines cover a wide range of issues, they are not able to address the needs of people with all types, degrees, and combinations of disability. These guidelines also make Web content more usable by older individuals with changing abilities due to aging and often improve usability for users in general.

WCAG 2.2 is developed through the W3C process in cooperation with individuals and organizations around the world, with a goal of providing a shared standard for Web content accessibility that meets the needs of individuals, organizations, and governments internationally. WCAG 2.2 builds on WCAG 2.0 [WCAG20] and WCAG 2.1 [WCAG21], which in turn built on WCAG 1.0 [WAI-WEBCONTENT] and is designed to apply broadly to different Web technologies now and in the future, and to be testable with a combination of automated testing and human evaluation. For an introduction to WCAG, see the Web Content Accessibility Guidelines (WCAG) Overview.

Significant challenges were encountered in defining additional criteria to address cognitive, language, and learning disabilities, including a short timeline for development as well as challenges in reaching consensus on testability, implementability, and international considerations of proposals. Work will carry on in this area in future versions of WCAG. We encourage authors to refer to our supplemental guidance on improving inclusion for people with disabilities, including learning and cognitive disabilities, people with low-vision, and more.

Web accessibility depends not only on accessible content but also on accessible Web browsers and other user agents. Authoring tools also have an important role in Web accessibility. For an overview of how these components of Web development and interaction work together, see:

Essential Components of Web Accessibility
User Agent Accessibility Guidelines (UAAG) Overview
Authoring Tool Accessibility Guidelines (ATAG) Overview
Where this document refers to WCAG 2 it is intended to mean any and all versions of WCAG that start with 2.
Authored by: Vijaya
Monday, May 6, 2024
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Posted on: What are the 12 Agile Principles?
Disciplinary Content
Tuesday, Jul 29, 2025
The Agile Alliance defines 12 lightness principles for those who need to attain agility: Our highes
Edited: New: The Agile Alliance defines 12 lightness principles for those who need to attain agility:

Our highest priority is to satisfy the client through early and continuous delivery of valuable computer software.
Welcome dynamic necessities, even late in development. Agile Processes harness modification for the customer’s competitive advantage.
Deliver operating computer software often, from a pair of weeks to a couple of months, with a preference to the shorter timescale.
Business individuals and developers should work along daily throughout the project.
The build comes around actuated people. offer them the setting and support they have, and trust them to urge the task done.
the foremost economical and effective methodology of conveyancing info to and among a development team is face-to-face speech.
Working with computer software is the primary life of progress.
Agile processes promote property development. The sponsors, developers, and users will be able to maintain a relentless pace indefinitely.
Continuous attention to technical excellence and smart style enhances nimbleness.
Simplicity—the art of maximizing the number of work not done—is essential.
the most effective architectures, necessities, and styles emerge from self–organizing groups.
At regular intervals, the team reflects on a way to become simpler, then tunes and adjusts its behavior consequently.
Authored by: Chathuri Super admin..
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Posted on 1: What are the 12 Agile Principles?
The Agile Alliance defines 12 lightness principles for those who need to attain agility: Our highes
Edited: New: The Agile Alliance defines 12 lightness principles for those who need to attain agility:

Our highest priority is to satisfy the client through early and continuous delivery of valuable computer software.
Welcome dynamic necessities, even late in development. Agile Processes harness modification for the customer’s competitive advantage.
Deliver operating computer software often, from a pair of weeks to a couple of months, with a preference to the shorter timescale.
Business individuals and developers should work along daily throughout the project.
The build comes around actuated people. offer them the setting and support they have, and trust them to urge the task done.
the foremost economical and effective methodology of conveyancing info to and among a development team is face-to-face speech.
Working with computer software is the primary life of progress.
Agile processes promote property development. The sponsors, developers, and users will be able to maintain a relentless pace indefinitely.
Continuous attention to technical excellence and smart style enhances nimbleness.
Simplicity—the art of maximizing the number of work not done—is essential.
the most effective architectures, necessities, and styles emerge from self–organizing groups.
At regular intervals, the team reflects on a way to become simpler, then tunes and adjusts its behavior consequently.
DISCIPLINARY CONTENT
Authored by: Chathuri Super admin..
Tuesday, Jul 29, 2025
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