This document provides an overview of speech recognition technology. It defines key terms like utterances, pronunciation, and grammar. It describes how speech recognition works by explaining the acoustic model, grammar, and recognized text. It also discusses standards, performance measurement, and provides an example of Google Search by Voice.
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
speech processing and recognition basic in data miningJimit Rupani
Basic presentation about speech processing
Name of the paper i read is :"An educational platform to demonstrate speech processing techniques on Android based smart phones and tablets" on Elsevier
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
Speech to text conversion for visually impaired person using µ law compandingiosrjce
The paper represents the overall design and implementation of DSP based speech recognition and
text conversion system. Speech is usually taken as a preferred mode of operation for human being, This paper
represent voice oriented command for converting into text. We intended to compute the entire speech processing
in real time. This involves simultaneously accepting the input from the user and using software filters to analyse
the data. The comparison was then to be established by using correlation and µ law companding techniques. In
this paper, voice recognition is carried out using MATLAB. The voice command is a person independent. The
voice command is stored in the data base with the help of the function keys. The real time input speech received
is then processed in the speech recognition system where the required feature of the speech words are extracted,
filtered out and matched with the existing sample stored in the database. Then the required MATLAB processes
are done to convert the received data and into text form.
speech processing and recognition basic in data miningJimit Rupani
Basic presentation about speech processing
Name of the paper i read is :"An educational platform to demonstrate speech processing techniques on Android based smart phones and tablets" on Elsevier
Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic –phonetic approach
Speech synthesis we can, in theory, mean any kind of synthetization of speech. For example, it can be the process in which a speech decoder generates the speech signal based on the parameters it has received through the transmission line, or it can be a procedure performed by a computer to estimate some kind of a presentation of the speech signal given a text input. Since there is a special course about the codecs (Puheen koodaus, Speech Coding), this chapter will concentrate on text-to-speech synthesis, or shortly TTS, which will be often referred to as speech synthesis to simplify the notation. Anyway, it is good to keep in mind that irrespective of what kind of synthesis we are dealing with, there are similar criteria in regard to the speech quality. We will return to this topic after a brief TTS motivation, and the rest of this chapter will be dedicated to the implementation point of view in TTS systems. Text-to-speech synthesis is a research field that has received a lot of attention and resources during the last couple of decades – for excellent reasons. One of the most interesting ideas (rather futuristic, though) is the fact that a workable TTS system, combined with a workable speech recognition device, would actually be an extremely efficient method for speech coding). It would provide incomparable compression ratio and flexible possibilities to choose the type of speech (e.g., breathless or hoarse), the fundamental frequency along with its range, the rhythm of speech, and several other effects. Furthermore, if the content of a message needs to be changed, it is much easier to retype the text than to record the signal again. Unfortunately this kind of a system does not yet exist for large vocabularies. Of course there are also numerous speech synthesis applications that are closer to being available than the one discussed above. For instance, a telephone inquiry system where the information is frequently updated, can use TTS to deliver answers to the customers. Speech synthesizers are also important to the visually impaired and to those who have lost their ability to speak. Several other examples can be found in everyday life, such as listening to the messages and news instead of reading them, and using hands-free functions through a voice interface in a car, and so on.
ACHIEVING SECURITY VIA SPEECH RECOGNITIONijistjournal
Speech is one of the essential sources of the conversation between human beings. We as humans speak and listen to each other in human-human interface. People have tried to develop systems that can listen and prepare a speech as persons do so naturally. This paper presents a brief survey on Speech recognition, allow people to compose documents and control their computers with their voice. In other words, the process of enabling a machine (like a computer) to identify and respond to the sounds produced in human speech. ASR can be treated as the independent, computer-driven script of spoken language into readable text in real time. The Speech Recognition system requires careful attention to the following issues: Meaning of various types of speeches, speech representation, feature extraction techniques, speech classifiers, and database and performance evaluation. This paper helps in understanding the technique along with their pros and cons. A comparative study of different technique is done as per stages.
Over 56 million hours of conversations are spoken a day in call centers worldwide, according to an industry report. If this collected audio data can be aggregated, speech analytics can yield quality insights into customer expectations, preferences and service issues. This whitepaper aims to illustrate basic technologies used in speech analytics, their use cases and how ROI from speech analytics software can be maximized.
Developing a hands-free interface to operate a Computer using voice commandMohammad Liton Hossain
The main focus of this study is to help a handicap person to operate a computer by voice command. It can be used to operate the entire computer functions on the user’s voice commands. It makes use of the Speech Recognition technology that allows the computer system to identify and recognize words spoken by a human using a microphone. This Software will be able to recognize spoken words and enable user to interact with the computer. This interaction includes user giving commands to his computer which will then respond by performing several tasks, actions or operations depending on the commands they gave. For Example: Opening /closing a file in computer, YouTube automation using voice command, Google search using voice command, make a note using voice command, calculation by calculator using voice command etc.
Voice Recognition System using Template MatchingIJORCS
It is easy for human to recognize familiar voice but using computer programs to identify a voice when compared with others is a herculean task. This is due to the problem that is encountered when developing the algorithm to recognize human voice. It is impossible to say a word the same way in two different occasions. Human speech analysis by computer gives different interpretation based on varying speed of speech delivery. This research paper gives detail description of the process behind implementation of an effective voice recognition algorithm. The algorithm utilize discrete Fourier transform to compare the frequency spectra of two voice samples because it remained unchanged as speech is slightly varied. Chebyshev inequality is then used to determine whether the two voices came from the same person. The algorithm is implemented and tested using MATLAB.
NLP Techniques for Speech Recognition.docxKevinSims18
Natural Language Processing (NLP) techniques are used to enhance the accuracy of speech recognition systems. NLP techniques are based on machine learning algorithms, which enable systems to learn from data and improve their performance over time. In this blog post, we will discuss some of the most popular NLP techniques that are used for speech recognition.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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Session Overview
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Knowledge engineering: from people to machines and back
Speech recognition an overview
1. Speech Recognition
An Overview
Submitted To:
Submitted By:
Name: Dr. M Syamala Devi
Name: Varun Jain
Designation: Professor
Roll No.: 34
Department: DCSA, Panjab
University, Chandigarh
Class: MCA 5th Semester (M)
Panjab University, Chandigarh
2. Table of Contents
Introduction
Introduction – A closer look
Terms and Concepts in Speech Recognition
Pronunciation
Utterances
Grammar
How it works
Acoustic Model
Grammar
Recognized Text
Performance Measurement
Standards
Google Search by Voice (GSbV) – A case study
GSbV – A screenshot
GSbV – An example
Conclusions
References
3. Introduction
Have you ever talked to your computer?
I mean, have you really, really talked to your computer?
Where it actually recognized what you said and then did something as a result?
If you have, then you've used a technology known as speech recognition.
It is the process of converting spoken input to text.
Speech recognition allows you to provide input to an application with your voice.
Speech recognition is thus sometimes referred to as speech-to-text.
In the desktop world, you need a microphone to be able to do this.
The speech recognition process is performed by a software component known as
the speech recognition engine.
The primary function of the speech recognition engine is to process spoken input
and translate it into text that an application understands.
4. Introduction – A closer look
Upon hearing a voice as input, a speech recognition application can do one of
the following thing:
The application can interpret the result of the recognition as a command. In this
case, the application is a command and control application. An example of a
command and control application is one in which the caller says “check balance”,
and the application returns the current balance of the caller’s account.
If an application handles the recognized text simply as text, then it is considered a
dictation application. In a dictation application, if you said “check balance,” the
application would not interpret the result, but simply return the text “check
balance”.
For example, in response to hearing "Please say coffee, tea, or milk," you say
"coffee" and the speech recognition application you're calling tells you what
the flavor of the day is and then asks if you'd like to place an order.
5. Terms and Concepts in Speech
Recognition
There are basically three terms that are majorly used in context of speech
recognition concept.
These three are:
Utterance
Pronunciation
Grammar
6. Utterance
When the user says something, this is known as an utterance.
An utterance is any stream of speech between two periods of silence.
Utterances are sent to the speech engine to be processed.
Silence, in speech recognition, is almost as important as what is spoken, because
silence delineates the start and end of an utterance.
The speech recognition engine is "listening" for speech input.
When the engine detects audio input - in other words, a lack of silence -- the
beginning of an utterance is signaled.
Similarly, when the engine detects a certain amount of silence following the
audio, the end of the utterance occurs.
If the user doesn’t say anything, the engine returns what is known as a silence
timeout - an indication that there was no speech detected within the expected
timeframe, and the application takes an appropriate action, such as re-prompting
the user for input.
7. Pronunciation
The speech recognition engine uses all sorts of data, statistical models, and
algorithms to convert spoken input into text.
One piece of information that the speech recognition engine uses to process a
word is its pronunciation, which represents what the speech engine thinks a
word should sound like.
Words can have multiple pronunciations associated with them.
For example, the word “the” has at least two pronunciations in the U.S.
English language: “thee” and “thuh.”
8. Grammar
One must specify the words and phrases that users can say to your
application.
These words and phrases are defined to the speech recognition engine and
are used in the recognition process.
You can specify the valid words and phrases in a number of different ways,
but in application, you do this by specifying a grammar.
A grammar uses a particular syntax, or set of rules, to define the words and
phrases that can be recognized by the engine.
A grammar can be as simple as a list of words, or it can be flexible enough to
allow such variability in what can be said that it approaches natural language
capability.
9. How it works
Fig. 1 A basic working model of speech recognition
depicting how a general speech recognition works.
10. Acoustic Model
Acoustic models provide an estimate for the likelihood of the observed
features in a frame of speech given a particular phonetic context.
The features are typically related to measurements of the spectral
characteristics of a time-slice of speech.
While individual recipes for training acoustic models vary in their structure
and sequencing, the basic process involves aligning transcribed speech to
states within an existing acoustic model, accumulating frames associated with
each state, and re-estimating the probability distributions associated with the
state, given the features observed in those frames.
11. Grammar
A grammar can be as simple as a list of words, or it can be flexible enough to
allow such variability in what can be said that it approaches natural language
capability.
Grammars define the domain, or context, within which the recognition engine
works.
The engine compares the current utterance against the words and phrases in the
active grammars.
If the user says something that is not in the grammar, the speech engine will not
be able to decipher it correctly.
You can define a single grammar for your application, or you may have multiple
grammars.
Chances are, you will have multiple grammars, and you will activate each
grammar only when it is needed.
12. Recognized Text
It is an output of this model that produces a likelihood if what user has
provided as input commonly known as recognized text.
This text can be processed in two way by the application using this model:
As a command
As a dictation text
As a command, a further processing is made upon the text which serves as
output
As a dictation text, a stream of text is shown as output as a formatted text
just like a text can be shown on screen.
13. Performance Measurement
The performance of a speech recognition system is measurable.
Perhaps the most widely used measurement is accuracy.
Arguably the most important measurement of accuracy is whether the desired
end result occurred.
This measurement is useful in validating application design.
For example, if the user said "yes," the engine returned "yes," and the "YES"
action was executed, it is clear that the desired end result was achieved.
But what happens if the engine returns text that does not exactly match the
utterance?
For example, what if the user said "nope," the engine returned "no," yet the
"NO" action was executed?
14. Standard
There are a lot of standards by World Wide Web Consortium (W3C)
Some of them are enlisted below:
VoiceXML
SRGS (Speech Recognition Grammar Specification)
SISR (Semantic Interpretation for Speech Recognition)
SSML (Speech Synthesis Markup Language)
PLS (Pronunciation Lexicon Specification)
CCXML (Call Control eXtensible Markup Language)
MediaCTRL
MSML (Media Server Markup Language)
MSCML (Media Server Control Markup Language)
15. Google search by voice: A case study
Mobile web search is a rapidly growing area of interest.
Internet-enabled Smart phones account for an increasing share of the mobile
devices sold throughout the world, and most models offer a web browsing
experience that rivals desktop computers in display quality.
Users are increasingly turning to their mobile devices when doing web
searches, driving efforts to enhance the usability of web search on these
devices.
Although mobile device usability has improved, typing search queries can still
be cumbersome, error-prone, and even dangerous in some usage scenarios.
17. GSbV – An example
Figure 3. An example of a speech recognizer app GSbV
18. Conclusions
Speech recognition will revolutionize the way people conduct business over
the Web and will, ultimately, differentiate world-class e-businesses.
VoiceXML ties speech recognition and telephony together and provides the
technology with which businesses can develop and deploy voice-enabled Web
solutions TODAY!
These solutions can greatly expand the accessibility of Web-based self-service
transactions to customers who would otherwise not have access, and, at the
same time, leverage a business’ existing Web investments.
Speech recognition and VoiceXML clearly represent the next wave of the Web.