Voice Recognition Using Matlab

7/29/2019 Voice Recognition Using Matlab

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INTRODUCTIONVoice recognition is the automated

recognition of human speech.

Digital Signal Processing take real-worldsignals like voice, audio, video, etc that can bedigitized and then mathematically

manipulated.The simulation focuses on feature

extraction followed by feature matching.


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FUNDAMENTAL APPROACHES TOVOICE RECOGNITION

1.template matching:Simple, accurate , uses analog-to-digital converters ,vocabulary based.

2.feature analysis:Speaker independent, first processes the voice input

using "Fourier transforms" or "linear predictive coding(LPC)", then finds characteristic similarities betweenthe expected inputs and the actual digitized voice input.


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FEATURE EXTRACTIONFirst step in voice recognition


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A DSP contains these key components:1.Program Memory 2. Data Memory3. Compute Engine 4. Input/Output

Two main processes in feature extraction1. Recording2. Playback


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WORD RATE &SAMPLING RATE

WORD SIZE :The number ofbits used to represent a single audiowave affects the achievable noise level of a signalrecorded

SAMPLING RATE:The sampling frequency used is equal to 11025 Hz.

The sample rate is even more important a consideration

than the word size. If the sample rate is too low, thesampled signal cannot be reconstructed to the originalsound signal.
http://en.wikipedia.org/wiki/Bithttp://en.wikipedia.org/wiki/Sound_wavehttp://en.wikipedia.org/wiki/Sound_wavehttp://en.wikipedia.org/wiki/Sampling_ratehttp://en.wikipedia.org/wiki/Sampling_ratehttp://en.wikipedia.org/wiki/Sound_wavehttp://en.wikipedia.org/wiki/Sound_wavehttp://en.wikipedia.org/wiki/Bit


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TEMPLATE MATCHING1. The user to speak a word or phrase into a microphone2. The electrical signal from the microphone is digitized

by an "analog-to-digital (A/D) converter", and isstored in memory.

3. The computer matches the input with a digitized voicesample, or template, that has a known meaning.

4. The program contains the input template to matchappropriately.

5. the program is "trained" with a new user's voice inputbefore that user's voice can be recognized by theprogram.

6. Limited to vocabulary that is stored.


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FEATURE ANALYSIS1. It is "speaker-independent" voice recognition.2. processes the voice input using "Fourier

transforms" or "linear predictive coding(LPC)

3. Find characteristic similarities between theexpected inputs and the actual digitized voiceinput.

4. Need not be trained by each new user.


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APPLICATIONS

Healthcare: Speech recognition can be implementedin front-end or back-end of the medicaldocumentation process.

Military: In High-performance fighter aircraft,Helicopters, Training air traffic controllers.

Telephony and other domains

Hands-free computing: Speech recognitioncomputer

user interface Home automations

Interactive voice response


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Mobile telephony , including mobile email Multimodal interaction

Pronunciation evaluation in computer-aidedlanguage learning applications

Robotics Speech-to-text reporter (transcription of speech

into text, video captioning, Court reporting )

Telematics (e.g., vehicle Navigation Systems)

Transcription (digital speech-to-text) Video games

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Voice Recognition Using Matlab