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Tags: SPEECH, RECOGNITION, USING, full, report, SPEECH RECOGNITION USING DSP, SPEECH RECOGNITION USING DSP ppt, SPEECH RECOGNITION USING DSP pdf, SPEECH RECOGNITION USING DSP seminar, SPEECH RECOGNITION USING DSP seminar report, speech recognition ppt, speech recognition project, speech recognition algorithm, speech recognition using matlab, speech recognition system, speech recognition technology, speech recognition books, speech recognition matlab,
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Post: #1

.doc  SPEECH RECOGNITION USING DSP full report.DOC (Size: 88.5 KB / Downloads: 1387)

This paper deals with the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. Speaker recognition methods can be divided into text-independent and text-dependent methods. In a text-independent system, speaker models capture characteristics of somebody's speech, which show up irrespective of what one is saying. In a text-dependent system, on the other hand, the recognition of the speaker's identity is based on his or her speaking one or more specific phrases, like passwords, card numbers, PIN codes, etc. This paper is based on text independent speaker recognition system and makes use of mel frequency cepstrum coefficients to process the input signal and vector quantization approach to identify the speaker. The above task is implemented using MATLAB. This technique is used in application areas such as control access to services like voice dialing, banking by telephone, database access services, voice mail, security control for confidential information areas, and remote access to computers.

Principles of Speaker Recognition
Speaker recognition can be classified into identification and verification. Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker verification, on the other hand, is the process of accepting or rejecting the identity claim of a speaker. Figure 1 shows the basic structures of speaker identification and verification systems.
At the highest level, all speaker recognition systems contain two main modules (refer to Figure 1): feature extraction and feature matching. Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker. Feature matching involves the actual procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers.
Post: #2
could u just mail me the full notes with diagram at dears.1988[at]
Post: #3
please use to download i hope inside diagrams and images included
Post: #4
please send me the full report of this topic
Post: #5
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Post: #6
Speech Recognition System
The speech recognition system is a completely assembled and easy to use programmable speech recognition circuit. Programmable, in the sense that you train the words (or vocal utterances) you want the circuit to recognize. This board allows you to experiment with many facets of speech recognition technology. It has 8 bit data out which can be interfaced with any microcontroller for further development. Some of interfacing applications which can be made are controlling home appliances, robotics movements, Speech Assisted technologies, Speech to text translation, and many more.

•Self-contained stand alone speech recognition circuit
•User programmable
•Up to 20 word vocabulary of duration two second each
•Non-volatile memory back up with 3V battery onboard.
Will keep the speech recognition data in memory even
after power off.
•Easily interfaced to control external circuits &

Speech recognition will become the method of choice for controlling appliances, toys, tools and computers. At its most basic level, speech controlled appliances and tools allow the user to perform parallel tasks (i.e. hands and eyes are busy elsewhere) while working with the tool or appliance. The heart of the circuit is the HM2007 speech recognition IC. The IC can recognize 20 words, each word a length of 1.92 seconds.

Using the System
The keypad and digital display are used to communicate with and program the HM2007 chip. The keypad is made up of 12 normally open momentary contact switches. When the circuit is turned on, “00” is on the digital display, the red LED (READY) is lit and the circuit waits for a command. Training Words for Recognition Press “1” (display will show “01” and the LED will turn off) on the keypad, then press the TRAIN key ( the LED will turn on) to place circuit in training mode, for word one. Say the target word into the onboard microphone (near LED) clearly. The circuit signals acceptance of the voice input by blinking the LED off then on.

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Post: #7
A DSP Implementation of Embedded Zerotree
Wavelet (EZW) Image CODEC in Image
Compression System

Digital image compression is a very popular topic in the field of multimedia processing. The major focus of work is to develop different compression schemes/algorithms to provide good visual quality fewer bit to represent an image in digital format.

This paper work describes software and hardware implementation of Embedded Zerotree Wavelet (EZW) image Coder-Decoder (CODEC).The EZW image CODEC is used to compress and decompress gray scale image in image compression system. Here, EZW CODEC is implemented by Texas Instruments Digital Signal Processor (DSP) TMS320C6713 board. The EZW CODEC algorithms has been transferred into C and assembly code in Code Composer Studio (CCS) in order to program the C6713 DSP processor. The statistical analysis is also carried out with profile statistic available in CCS environment.

With the development tools provided for the C6713 DSP platform, it created easy-to-use environment that optimizes the devices’ performance and minimizes technical barriers to software and hardware design


With the growth of technology and the entrance into the Digital Age, the world has found itself a vast amount of information. Dealing with such enormous amount of information can often present difficulties. Digital information must be stored, retrieved, analyzed and processed in an efficient manner, in order for it to be put to practical use.

A. Need for Compression

Today is an era of internet, images, motion pictures etc. Transmission and usage of image can not be avoided. Image compression is a technique which makes storage and transmission of images more practical. the basic requirement of maintaining image quality is easily translated into two basic quantitative parameters:

1) Rate of digital image data transfer or data rate (Megabit per second or Mb/s)

2) Total amount of digital storage required or data.
With image compression both data rate and data capacity are reduced to great extent. So less space, less time and less bandwidth are required for storage and transmission of digital images.
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Post: #8
hi sir mail me detai seminar report on speech recognisationusing DSP
Post: #9
sorry pooja pattanakude. we can't mail you. please go through the thread carefully. this thread contain a bundle of information on
speech recognisationusing DSP. download attached files for more information. if that is not enough to make a good report, we are here to help you.
Post: #10
Presented by:
G.Nagendra babu

.ppt  NAGENDRA.ppt (Size: 584 KB / Downloads: 122)
All speaker recognition systems contain two main modules feature extraction and feature matching.
1.Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker.
2.Feature matching involves the procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers.
Fast Fourier Transform (FFT) :
The next processing step is the Fast Fourier Transform, which converts each frame of N samples from the time domain into the frequency domain. The FFT is a fast algorithm to implement the Discrete Fourier Transform (DFT) which is defined on the set of N samples {xn},
 Even though much care is taken it is difficult to obtain an efficient speaker recognition system since this task has been challenged by the highly variant input speech signals.
 Speech signals in training and testing sessions can be greatly different due to many facts such as people voice change with time, health conditions (e.g. the speaker has a cold), speaking rates, etc.
 There are also other factors, beyond speaker variability, that present a challenge to speaker recognition technology. Because of all these difficulties this technology is still an active area of research
Post: #11
hi,i need speech recognition (text dependent) system full documentation which is used for my mini-project. it is necessary to me
Post: #12
To get more information about the topic "SPEECH RECOGNITION USING DSP " please refer the link below
Post: #13
Plz can u send me full report of speech recognition at ingale.pravin27[at] :)[/size][/font]
Post: #14
To get more information about the topic "SPEECH RECOGNITION USING DSP " please refer the link below
Post: #15
I mant the seminar report for this topic

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