speed artificial intelligence Speech (definition & types) Artificial intelligence (definition & its types) Speech-Artificial Intelligence (Definition) Types of Recognition Working of Speech recognition Techniques Involved In speech Recognition  Speech recognition software's  Guideline to start future. Future of Artificial Intelligence  References

2. Speech-Artificial Intelligence
Submitted To: Muhammad Ikram
Submitted By:
Khawaja Muhammad
Rizwan (17581556-011)

3. Contents
1. Speech (definition & types)
2. Artificial intelligence (definition & its types)
3. Speech-Artificial Intelligence (Definition)
4. Types of Recognition
5. Working of Speech recognition
6. Techniques Involved In speech Recognition
7. Speech recognition software's
8. Guideline to start future.
9. Future of Artificial Intelligence
10.References

4. I • Speech
It is communication through talking or a talk given to an audience.
• Example
• An example of speech is a conversation between two people.
An example of speech is the presidential address.
• Types of speech
• There are eight parts of speech in the English language: noun, pronoun,
verb, adjective, adverb, preposition, conjunction, and interjection.
• Artificial Intelligence
• Artificial Intelligence (AI) is a broad branch of computer science, aiming to
create systems that can function intelligently and independently just like
humans. AI is an imitation of human intelligence processes by machines. ...

5. I

6. I • Speech-Artificial Intelligence :
• Speech recognition is one such technology that is empowered by AI to add
convenience to its users. This new technology has the power to
convert voice messages to text. And it also has the ability to recognize an
individual based on their voice command.
• Recognition

7. I • Acoustic Model
• An acoustic model is created by taking audio recordings of speech,
and their text transcriptions, and using software to create statistical
representations of the sounds that make up each word. It is used by
a speech recognition engine to recognize speech.
• Language Model
• Language model is used in many natural language
processing applications such as speech recognition tries to capture
the properties of a language, and to predict the next word in a speech
sequence.

8. I • Types of speech recognition. (Modelling Technique)
1. Speaker-Dependent
• is commonly used for dictation software
2. Speaker-Independent
• is more commonly found in telephone applications.
• Speaker-dependent
• Software works by learning the unique characteristics of a single person’s
voice, in a way similar to voice recognition.
• New users must first “train” the software by speaking to it, so the computer
can analyze how the person talks.
• This often means users have to read a few pages of text to the computer
before they can use the speech recognition software.

9. I
• Speaker-independent
• It’s a software which is designed to recognize anyone’s voice, so no
training is involved.
• This means it is the only real option for applications such as interactive
voice response systems — where businesses can’t ask callers to read
pages of text before using the system.
• The downside is that speaker-independent software is generally less
accurate than speaker-dependent software.
• Speech recognition engines that are speaker independent generally
deal with this fact by limiting the grammars they use. By using a
smaller list of recognized words, the speech engine is more likely to
correctly recognize what a speaker said.

10. I
• How Computer do Speech Recognition

11. I

12. I
• Techniques Involved In speech Recognition
• Analysis
• Feature Extraction
• Modelling
• Matching
• Testing
• Best speech recognition software
• Dragon Professional.
• Dragon Anywhere.
• Google Now.
• Google Cloud Speech API.
• Google Docs Voice Typing.
• Siri.

13. I
• Requirements to Start carrier in AI
• Programming language Skills (python, C++ etc.)
• Having a basic knowledge of networks and patterns
• Good in Mathematics used in patterns matching
• Knowledge of Algorithms.
• Future of Artificial intelligence
• Healthcare:
In the comparatively AI-nascent field of healthcare, diseases are more quickly
and accurately diagnosed, drug discovery is sped up and streamlined, virtual nursing
assistants monitor patients and big data analysis helps to create a more personalized
patient experience.
• Education:
Textbooks are digitized with the help of AI, early-stage virtual tutors assist
human instructors and facial analysis gauges the emotions of students to help determine
who’s struggling or bored and better tailor the experience to their individual needs .

14. I
• References
• https://builtin.com/artificial-intelligence/artificial-intelligence-future
• https://usabilitygeek.com/automatic-speech-recognition-asr-software-an-introduction/
• https://d1wqtxts1xzle7.cloudfront.net/49255272/A_Review_on_Speech_Recognition
_Technique20160930-21561-nv0vxq.pdf?1475291465=&response-content-
disposition=inline%3B+filename%3DA_Review_on_Speech_Recognition_Technique
.pdf&Expires=1607534462&Signature=NTabyzz9eEs02eNJCPjVmRzCIvCDpPjrm5C
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FySDgBQ01b8FtBbWT60Cv9zyZUKgXTAbjFZaMzjCBFT4BdGw__&Key-Pair-
Id=APKAJLOHF5GGSLRBV4ZA
• https://www.youtube.com/watch?v=KJ23aCs-
wwc&list=PLwgFb6VsUj_lQTpQKDtLXKXElQychT_2j&index=8
• https://builtin.com/artificial-intelligence/ai-in-education

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