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Lpc Coding. Chapter 1 Introduction 1.1 Speech Production When you speak,
Air is pushed from your lung through your vocal tract and ...
... which follows. Another method, which is used to obtain a frequency spectrum
is that of Linear Predictive Coding(LPC). This is the ...
... GSM uses linear predictive coding (LPC). The purpose of LPC is to reduce the bit
rate. The LPC provides parameters for a filter that mimics the vocal tract. ...
Submitted by palmy777 on May 12, 2008
Category: Technology
Words: 6266 | Pages: 26
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Chapter 1
Introduction
1.1 Speech Production
When you speak, Air is pushed from your lung through your vocal tract and out of your mouth comes speech.
For certain voiced sound, your vocal cords vibrate (open and close). The rate at which the vocal cords vibrate determines the pitch of your voice. Women and young children tend to have high pitch (fast vibration) while adult males tend to have low pitch (slow vibration).
For certain fricatives and plosive (or unvoiced) sound, your vocal cords do not vibrate but remain constantly opened.
The shape of your vocal tract determines the sound that you make. As you speak, your vocal tract changes its shape producing different sound.
The shape of the vocal tract changes relatively slowly (on the scale of 10 msec to 100 msec).
The amount of air coming from your lung determines the loudness of your voice.
1.2 Speech Signal
Speech signal carries with it both message and speaker information. Speech is used to convey the message through a sequence of sound units, which are produced by exciting the time varying vocal tract system with time varying excitation. Each sound unit is produced by a specific combination of excitation and vocal tract dynamics. For representation of speech message information, the vocal tract system is modeled as a time varying filter, and the excitation as voiced or unvoiced or plosive or combination of these types. The time varying filter characteristics capture the variations in the shape of the vocal tract system in the form of resonances, antiresonances, and spectral roll-off characteristics. This representation of speech has been very effective for developing speech recognition systems. Since the vocal tract shape and its dynamics are also unique for a given speaker, the same time varying filter representation has been exploited for developing speaker recognition...
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