Speech Recognition for Home Appliances

What is Speech Recognition?The technology by which sounds, words or

phrases spoken by humans are converted into electrical signals, and these signals are transformed into coding patterns to which meaning has been assigned.

This project will aim to create a device which accepts audio input and will send a corresponding signal to another device to be controlled to perform the required task.

ComponentsMicrophone.FGPA (Altera DE2-115).Bluetooth Module.Microcontroller (Arduino UNO with

ATmega328).5V Relays.

Bluetooth module

Altera de2-115

5V RelaysArduino UNO

Block Diagram

InputThe voice command is given to the

microphone.The Altera board has a 24-bit Audio CODEC

microphone interface so that we directly plug in the microphone.

The electrical signal from the microphone is digitized by an "analog-to-digital (A/D) converter", and is stored in memory.

ProcessingA typical speech recognition system has two

stages• A pre-processing stage, which takes a speech

waveform as its input, and extracts from it feature vectors or observations which represent the information required to perform recognition.

• The next stage is recognition, or decoding, which is performed using a set of phoneme-level statistical models called hidden Markov models (HMMs).

ApproachesThe most common approaches to voice recognition

can be divided into two classes: "template matching" and "feature analysis".

Template matching: We match the input with a digitized voice

sample, or template.Feature analysis:

We first processes the voice input using "Fourier transforms" or "linear predictive coding (LPC)", then attempt to find similarities between the expected inputs and the actual digitized voice input.

Preferred methodology:HMM-Hidden Markov modelHidden Markov Models (HMMs) provide a simple

and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs.

For small- to medium-sized vocabularies the word and language models are compiled into a single, integrated model. Recognition is performed using the Viterbi algorithm to find the route through this model which best explains the data.

Hardware ImplementationThe final implementation would be as follows:

A remote would consist of a power button, led and microphone externally. Internally it consists of the ASIC and Bluetooth module.

At the other end, we would be having a microcontroller (ATmega8) which would also have a Bluetooth module and is attached to other appliances through relay. (must be integrated with the house wiring)

Wireless CommunicationThe output from the previous stage is fed to

the Bluetooth module, which is received by another Bluetooth module .

This Bluetooth module is connected to microcontroller.

We program the microcontroller to do the required operation.

ExtensionsControl of televisions with Wi-Fi.Control of television without Wi-Fi.

Implementation with RF module so that cost could go as low as Rs.1,200.

Cost ExpectedComponent Price

Microphone Rs. 600

Altera de2-115 Rs. 20,000

Bluetooth modules Rs. 2,500

Arduino UNO Rs. 2,000

5V Relays (6) Rs. 250

Other Components Rs. 1,000

Total Rs. 27,000*

Expected Cost of the Product in bulkComponent Price

ASIC Rs. 300

Microphone Rs. 200

Bluetooth modules Rs. 2,000

ATmega8 Rs. 85

5V Relays (6) Rs. 150

Other Components Rs. 50

Total Rs. 2,785*

ReferencesSpeech Recognition on an FPGA using Discrete and

Continuous Hidden Markov ModelsBy: Stephen J. Melnikoff, Steven F. Quigley & Martin J. RussellElectronic, Electrical and Computer Engineering,

University of Birmingham

Speaker-Independent Phone Recognition Using Hidden Markov Models

By: Kai-Fu-Lee, member, IEEE and HSIAO-WUEN HON