The document discusses voice recognition using MatLab. It introduces voice recognition as the process of converting acoustic signals to words. Voice recognition can be used for transcription, command and control, and information access. It discusses the principles and methods of voice recognition, including text-dependent and text-independent approaches. The document outlines the key components of a voice recognition system, including feature extraction using mel-frequency cepstrum coefficients (MFCC), recognition models, and applications like device control and mobile phones. It also reviews the advantages, limitations, and future of voice recognition technology.

1. Voice Recognition
Using MatLab
Presented by: Avienash raibole
Paresh Meshram
Vinayak kolpek

2.  INTRODUCTION
• The purpose of our project is to implement an
efficient voice recognition algorithm using
MatLab.
• Voice recognition is the process of converting
an acoustic signal, captured by a microphone
or a telephone, to a set of words.
• The recognised words can be an end in
themselves, as for applications such as
commands & control, data entry, and
document preparation.
• They can also serve as the input to further
linguistic processing in order to achieve
speech understanding.

3.  What we can do with voice
Recognition
• Transcription
– dictation, information retrieval
• Command and control
– data entry, device control, navigation, call routing
• Information access
– airline schedules, stock quotes, directory
assistance
• Problem solving
– travel planning, logistics

4.  PRINCIPLE

5.  Speaker Recognition methods
Text Dependent :
For speaker identity is based on his/ her
speaking one or more specific phase.
Text Independent:
Speaker models capture characteristics of
somebody’s speech which show up
irrespective of what one is saying.

6.  BLOCK DIAGRAM
Frame
Blocking Windowing FFT
Cepstrum
Mel-frequency
wrapping
Continuous
speech
frame
spectrum
mel
cepstrum

7.  RECOGNITION MODELS

8.  Feature Extraction
• That extracts a small amount of data from the
voice signal that can later be used to
represent each speaker.
• A wide range of possibilities exist for
parametrically representing the speech signal
for the speaker recognition task, such as
a)Linear Prediction Coding(LPC),
b)Mel-Frequency Cepstrum Coefficients
(MFCC), and others.

9.  MFCC
• It is based on the known variation of the
human ear’s critical bandwidths with
frequency, filters spaced linearly at low
frequencies and logarithmically at high
frequencies.
• To capture the phonetically important
characteristics of speech, signal is expressed in
the Mel frequency scale .

10.  SIMPLE REPRESENTATION OF MFCC

11.  CALCULATION OF MFCC

12.  How does it work?
record extract
a voice feature
vectors
• Record voice command (Time domain).
• Transform into frequency domain using
Fourier Transform and get the magnitude
spectrum.
• Compare spectrum of voice commands.
Digitized
Speech
Signal
(.wave
file)
Acoustic
Preprocessing
(DFT + MFCC)
Speech
Recognizer
(Dynamic Time
Warping)

13.  Applications
• Controlling of device.
• Hands-free mobile phone in car.
• Single purpose command and control system.
• Voice Verification.
• Many more.

14.  Advantages
• The model is trained much faster than other
method.
• It is able to reduce large datasets to a smaller
number of codebook vectors.
• Easy to implementation and more accurate.
• Speech is a very natural way to interact, and it
is not necessary to sit at a keyboard or work
with a remote control.
• No training required for users.

15.  Limitations
• The amount of words that could be recognized
by our program was limited, the more words
we tried adding, the less accurate it became.
• The voice recognition program only works for
the person’s voice who is trained for it.
• Program is less accurate in noisy
environments.
• Voice Recognition works best if the
microphone is close to the user.

16.  Future Of Voice Recognition
• Better rejection of extraneous speech.
• Better recognition of embedded commands.
• Better efficiency on low cost processors.
• Standards for performance evaluation.
• Increased portability.
• Lower error rates.
• Improve overall robustness.

17.  Research Articles on Speech
Recognition
• Koester, H.H. (2006). Factors that Influence the Performance
of Experienced Speech Recognition Users. Assistive
Technology, 18(1): 56-76.
• Koester, H.H. (2004). Usage, Performance, and Satisfaction
Outcomes for Experienced Users of Speech Recognition.
Journal of Rehabilitation Research and Development, 41(5):
739-754.
• Koester, H.H. (2003). Abandonment of Speech Recognition
Systems. by New Users. Proceedings of RESNA 2003 Annual
Conference, Atlanta, GA. Arlington, VA: RESNA Press.
• Koester, H.H. (2002). User Performance with Speech
Recognition Systems: A Literature Review. Assistive
Technology, 13(2):116-30.

18. THANK
YOU…