Knowledge Base approach for spoken digit recognition

Vijetha Periyavaram

Speech Recognition Systems

Provides a vehicle for communication between people and machines

The exchange of information with machines is actually the complex product of more than 30 years of research in statistics, physics, linguistics, and computer science.

Characters in science fiction stories have conversed with robots and computers for a long time.

Speech Recognition Systems

•We may have shared a few wordswith a computer, car, or cell phonewhen they are not working properly

•But now these machines can understand and can respond because of speech recognition systems

Advantages

As a result of speech recognition systems you can

Ask your car for directions Dial your mobile phone with out touching it. Dictate a term instead of typing it on the keyboard Give commands to a personal organizer e.g:

Shutdown , pop up start menu etc…

Main Concept of Speech Recognition Systems

Speech recognition systems first break down spoken language into phonemes

For example: /w/ as in "we" "quite" "once"   /ch/ as in "much" "nature" "match" /ou/ as in "no" "boat" "low"   /au/ as in "haul" "bought" "draw"

Almost 40 phonemes

Main Concept

•The system converts the individual sounds into digitized sound waves ,which it matches with a built in dictionary

•The speech recognition system figures out the correct choice through a series of algorithms, or mathematical models, that help narrow down the possibilities to ones that make the most sense

Proposed Method

One of the method proposed here for speech recognition systems is Knowledge base approach for spoken digit recognition.

In this method digitized data is processed using MATLAB – DSP tool box.

Problem definition

To develop a system that can identify an isolated spoken digit based on the knowledge developed by analyzing the digits

Analysis is based on on the following features which can be extracted using Matlab – DSP tool kit

– Energy envelope: Plots the energy of the wave– Zero crossing rate: No. of times in a sound sample that

amplitude of the sound wave changes sign

Proposed solution

Utterances of different people are studied Knowledge base for digits is created from

above Each digit has unique characteristics

irrespective of speaker’s nationality because this method mainly concentrates on the phonemes

Analyzing few features of these spoken words the digits are recognized

The output is printed on the screen.

Scope of the system

Isolated word vocabulary Unlimited speaker population, unrestricted by age or

sex Computer room speaking environment Transmission over high quality microphone No prior training Single word format with pauses between each spoken

input

Technique used

Speech signal is sampled at a particular frequency

End points of isolated words detected Data time normalized All digits set to same number of data Zero crossing rate and energy envelope

determined from each segment

Data Acquisition

Records voice from the user using multimedia sound recording equipment

Data is digitized

Sampled at the rate mentioned

Recorded speech is plotted

Recorded wave for digit zero

Filtering the recorded data

Required because of presence of environmental, system, and inherent microphone noises

Uses elliptical band pass filter in range of human voice frequency

Output of filter for digit zero

Plotting energy envelope

Plots energy of spoken digit This is smoothened using moving point

average method– A 200-point moving point average chosen– Replaces each sample’s amplitude with average of

200 consecutive samples

Energy envelope for digit zero

Location of start and end points

Start and end points of envelope of spoken digit identified

Criterion: less than 10% of maximum value of the energy envelope is not considered

Actual message for digit zero

Time normalization of data

Envelope resampled so that spoken word always contains 6000 samples

Envelope smoothened using moving point average method

Resampled envelope for digit zero

Various wave forms for digit seven

FLOW CHART

Start

Wave Recording

(8000samples)

Filtering (Band pass of 400 –

3200HZ)

Energy Calculation

FLOW CHART

Smoothening (moving point

average Filter of 200 pts)

Determination of Start and end

points

Calculation of Zero crossing

rate

Resampling to 6000 samples

FLOW CHART

Calculation of no.of peaks and peak positions

Classification Algorithm

Stop

Setting up the knowledge base

Number of peaks in energy envelope of spoken digit

Energy peak level Energy peak positions Zero crossing rate for each segment

Classification

First sweep – counting number of peaks in energy envelope

– Single peak – Two peaks – Three peaks

Second sweep – peak positions Third sweep – zero crossing rate

Classification

Results

The system was tested for 100 different human voice signals and the success rate was 89%

The final output was displayed on the monitor as well as on the LCD screen

The response time was 7 seconds.

Few More Applications

Speaker Identification system Security systems Robot Control Bank Transactions Aircraft control system Stock price quotation system