1. A11
3062
1
University of Pittsburgh
Swanson School of Engineering March 7, 2013
DEVELOPMENTS IN VOICE REGONITION TECHNOLOGY
Ronald Buttermore (rwb31@pitt.edu, 0012 Bon 6:00), Naseem Lee-Perkins (nrl28@pitt.edu, 0012 Budny 10:00)
Abstract – Voice recognition software is “the process of
taking the spoken word as an input to a computer program”
[1]. Through this software, the goal is to be able to control
a computer efficiently and intuitively [2]. Voice recognition,
often only thought of as a way to command a cell phone, has
many beneficial applications which can be expanded once
improved to a more user friendly and accurate product.
In this paper, the objectives will be to explain and
analyze the importance of voice recognition and its
applications through detail of research and development of
and speech software, designs and implementations in
current and future technologies, and existing problems with
technologies and possible solutions. We will also detail the
impact of voice recognition’s integration into engineering
and society.
Voice recognition software and its improvement, in ease
of use and accuracy, could benefit the lives of many people,
as the applications of the technology would increase. In
addition, these applications have great potential with
respect to sustainability and considering the needs of future
generations along with meeting our own. These applications
work towards benefiting the consumer and corporations
alike. Voice recognition can allow users to go hands free
and result in faster interactions. Companies that utilize the
technologies may experience more productive workers, with
higher output. Ultimately voice recognition creates a more
appealing product which should generate new sales not only
for companies making the software but also for companies
using the software.
To achieve our objective of displaying the importance of
voice recognition technologies we will start by describing
the process about how speech recognition works from a
technical standpoint. We will be thoroughly outlining all
pros and cons of the development of this software. Finally,
we will analyze the impact of this technology on society. In
conclusion, this paper will highlight the benefits of voice and
speech recognition technologies by analyzing current
products, how voice recognition works, and its societal and
ethical impact.
Key Words – Acoustic Model, HMM model, Language
Model, Linguistic Interpretations, User Interface, Voice
Recognition
VOICE RECOGNITION SYSTEMS
Voice recognition systems are electronic systems that
help electronic devices respond to speech. Voice recognition
systems have many uses but with current flaws voice
recognition systems do not reach their full potential. This
paper will focus on the critique of voice recognition systems
and possible improvements to be made to the current
software, which will increase the uses and benefits of the
software.
We will begin by explaining how voice recognition
works. We will include hardware requirements and
descriptions of different techniques used by different
software to carry out these models.
A description of multiple uses by many different
individuals will be included to explain the importance and
benefits of voice recognition software. These uses will prove
the importance of voice recognition software and illustrate
why flaws and the improvement of flaws are necessary.
However, there are already proposed solutions for these
flaws that will increase the effectiveness of the systems
which will benefit many people. Recent developments in
modeling speech through improving recognition models has
helped voice recognition move forward.
These recent improvements have opened up possibilities
of new and reworked technologies that apply voice
recognition software. These technologies have the potential
to benefit and help progress society and its processes,
specifically through the sustainable increase in quality of
life.
WHAT IS VOICE RECOGNITION AND
HOW DOES IT WORK?
A voice recognition system translates spoken word to
digital signals that are processed to perform specific tasks.
However, voice recognition systems have requirements for
hardware and software in order to undergo this process.
Requirements for Voice Recognition Software
Basic voice recognition systems require 200 megahertz
Pentium processor, a minimum of 64 megabytes of RAM, a
basic microphone, and a minimum of a 16-bit sound card
[2]. Although most voice recognition will run, improvements
in these requirements can make a dramatic difference in the
performance of the voice recognition software. Increasing
the size of the RAM, processor, and sound card as well as
investing in a better microphone can help in the
improvement of the voice recognition system [2]. Along
with these hardware requirements, voice recognition requires
the use of software to collect, analyze, and interpret the data.
Different software approaches this process in different ways.
Acoustic and Language Models

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The acoustic and language models are the basic
processes in which sound is taken from the microphone and
processed through the computer.
In the acoustic model the voice is analyzed. After the
user speaks into the microphone, background noise and
unnecessary changes in volume are removed. Mathematical
calculations are used to take the voice and convert it to a
range of frequencies, which correspond to pitches in sound.
The data is then analyzed and converted to digital
representations of phonemes, which are the basic sounds of
language [2].
Next, the language model analyzes the content of speech.
The language model compares the combinations of
phonemes to words in a dictionary, the database of the most
common words in the English language. Dictionaries, or
databases, of individual software may differ based on how
each developer designs the language model process. Once
the language model decides which word was spoken, the
digital signal of the word is ready to be displayed or carry
out its designated task [2].
While acoustic and language models are the basic ways
voice recognition systems work, each system has a different
way of approaching these systems. The design of software
by each developer creates different techniques for voice
recognition systems.
Template Matching
Template matching is a technique that uses user input
and is referred to as a user dependent method. It has the
highest accuracy, at 98%, but in turn has the most limitations
[3]. Template matching starts with asking the user to speak
several words or terms into a microphone. The words are
repeated multiple times and a statistical average of the
sample words is stored used as a template [3]. Other
programs may come already loaded with digital voice
sample templates. Once the user speaks into the microphone
the electrical signal is converted to a digital signal using an
analog to digital converter and is stored in the memory. The
analog to digital converter takes a physical quantity and
converts it to a digital number [2]. Digital signals are
compared to those of the templates, and meaning is deduced
producing a word [3]. Some software also uses pre-
programed rules to help find meaning [4].
Feature Analysis
Feature Analysis is the speaker independent technique of
voice recognition software. It processes voice input using
Fourier Transforms or Linear Predictive Coding. Fourier
Transforms make a “generic function”, based on a quantity
like time, and convert it to another function based on a
physical property like frequency [5]. Fragniere, van Schaik,
and Vittoz describe the objective of Linear Predictive
Coding as a way “to predict the current value of the signal
using a linear combination of previous samples, each
weighted by a coefficient [6].” These two processes mimic
digital signals of real voice and the software attempts to find
characteristic similarities between expected inputs and actual
digitized voice input [3]. The similarities produce results that
correspond to a word. These similarities are present for a
wide range of speakers, so no user training is required. Once
a more efficient speaker independent software, like feature
analysis, is developed the system would be able to account
for accents, speed of delivery, pitch, volume, and inflection
[3].
THE APPLICATIONS AND
IMPORTANCE OF VOICE RECOGNITION
SOFTWARE
Voice recognition can have many applications, which
can benefit the lives of many people. Uses of voice
recognition software include learning tools, corporate and
consumer uses, helping people with disabilities, and
government use.
Learning Uses and Benefits
Learning can also be benefited by the use of voice
recognition software. It can be used to improve a user’s first
language addressing issues like pronunciation which can be
affected by ethnicity, social class, and education [7].
Because voice recognition systems are often looking for
traditional pronunciation of a word, error messages can alert
users to incorrect pronunciation. With repeated use of voice
recognition software along with error messages, users can
learn and practice to correctly pronounce words. A similar
idea can help users learn a second language as software can
be programed to mimic accents of other languages [7].
Again with practice and error analysis, voice recognition
software can shape a user’s pronunciation and delivery of a
non-native language. Voice recognition can also be used to
help individuals with learning disabilities like dyslexia, as it
allows them to easily use a computer and encourages writing
[8].
Voice recognition software can help improve the
education level of many individuals through helping with
speech problems and learning disorders, and as a tool to
teach a second language. Also through aiding the learning
process of a second language, voice recognition systems
help break down cultural barriers, letting users connect with
people without a language barrier. This is an increasingly
important aspect of education as the world becomes more
and more diverse and it is important to be able to
communicate and connect with people from different areas
of the world. The importance of education and learning is a
basic need that could be improved by voice recognition
technology, which would also potentially improve the
quality of life for many people.

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Consumer Uses and Benefits
Voice recognition software can be utilized by the
everyday consumer. Consumers can use voice recognition
software to dictate emails, navigate applications on electrical
devices, create documents, and search the web [9]. Voice
recognition systems also help people with disabilities.
People with speech disorders and people who are deaf or
hard of hearing can benefit from voice recognition software
[10]. Voice recognition systems can help facilitate faster
interactions between the deaf or hard of hearing with others,
especially those who do not know sign language, as speech
can be displayed as it is spoken. Voice recognition systems
can also help people with motor problems, physical
disabilities, and learning disabilities because would allow
them to use electronic devices without having to use their
hands [8].
Voice recognition allows for hands free use of computers
and electric devices. This allows for faster interactions with
electric devices as options are no longer limited to screen
size and there is no need to thumb through pages of options.
Voice recognition also saves the consumer time when typing
is involved because words can often be dictated faster than
they can be typed.
With new laws that require drivers to use electronic
devices hands free, voice recognition systems are now more
useful in phones and other devices. The ability to control
these devices without the use of hands makes roads safer for
drivers and pedestrians. With safer roads, increased
productivity, and aid towards the disabled, voice recognition
software has obvious benefits towards the improvement of
quality of life.
Corporate Uses and Benefits
Many different professions can use voice recognition
systems to help improve productivity. Voice recognition can
help reporting, healthcare systems, and call centers.
Healthcare systems often use vice recognition software as a
way to quickly enter electronic health records [11]. Call
centers allows users to participate in different activities or
transactions without waiting to talk to a live representative.
Careers in reporting often require quick typing; however
with the use of voice recognition systems words can be more
easily recorded and processed.
The main benefit of voice recognition software for
companies is increased productivity. In healthcare, workers
can enter information faster. Call centers don’t have to be
flooded with calls, as computers can process human input
and deliver results, freeing up workers to complete other
tasks. Reporters no longer have to worry about quick typing
and shortening words, with the risk of error and later
misunderstanding, as speech can be recorded and processed
as it is spoken. Implementing voice recognition software into
companies can thus improve a company’s productivity.
Government Uses and Benefits
Voice recognition software can be implemented to help
the government. Many different government agencies use
voice recognition including law enforcement, legal
departments, and case working [6]. These agencies may use
voice recognition software in much of the way that corporate
companies do. Employees can write quicker, easily and
quickly search documents, and quickly navigate the internet
and computers.
However, the government can also use voice recognition
software in the military. The most common use of voice
recognition software in the military is in “command and
control” which allows a control of a device or a system
through spoken commands. The military also uses voice
recognition software for speaker detection, and speaker
verification [12]. This allows the military to detect when
someone is speaking and confirm the identity of that person.
Government agencies similar to companies also
experience some of the same benefits as they also experience
increased productivity. However, since government agencies
tend to deal with more important matters, the productivity
experience is more beneficial to the public. Military uses of
voice recognition present the public with benefits in safety
since voice recognition systems can improve and aid
operations. Implementing voice recognition systems in
“command and control” help keep soldiers safe, as they no
longer are required to enter some of the dangerous situations
where some of the control devices must go.
However, uses and benefits of voice recognition system
are currently limited because of design flaws in the certain
software, which process the voice input and analyzes it, and
hardware, which have yet to make full use of voice
recognition technology.
CURRENT FLAWS AND LIMITATIONS
While voice recognition software does work, there are
some flaws and limitations to the current software that can
be improved in order to make the systems more user-friendly
and effective.
One of the current flaws in some of the voice recognition
software is the use of word systems. Most word systems are
discrete or connected. This means they can only process one
word or short phrases, respectively. This system requires
users to use clear articulate speech, making sure not to slur
their words. Users must be especially careful not to slur the
ends of their words in connected word systems where it is
easy to slur the end of one word into the beginning of the
other [3]. This creates a problem with users as speech
becomes unnatural. These words systems present problems
for some uses of voice recognition software. One example of
this issue is in reporting where voice recognition software is
needed to rapidly record speech. However, speech may not
always be correctly pronounced or clearly articulated. While
the discrete and connected word systems are the easiest to

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implement, there is a third type of word system that can be
used, the continuous speech system. Unfortunately
continuous speech systems are the hardest to implement.
Another problem with voice recognition systems is
homophones [2]. Homophones, words that sound the same
but have different meaning, are difficult to distinguish in
voice recognition systems. Because voice recognition
systems match the patterns of the sounds of phonemes with
words in a dictionary, homophones present multiple choices
in the system and the correct word is not always selected.
While some voice recognition systems use statistical models
to select the most probable word, this does not always
present the correct word. Other systems attempt to use
trigrams, which analyze the context to decide which word to
use. However, if the system is also operating with a discrete
word system, which is only a single word, there isn’t any
context to help analyze the meaning of the word.
User dependent systems also present multiple problems
for voice recognition systems [2]. Because user dependent
systems need to be adjusted by the user in order to become
familiar with an accent or speech pattern, they are not
practical for widespread use. This presents problems for
professional uses like reporting or call-centers, where a wide
range of accents, dialects, and speech patterns need to be
understood.
Not only would the flaws keep companies from
effectively completing tasks but in cases like a call center,
where there is client interaction, clients may grow frustrated
with the process and company. These flaws can also hinder
learning efforts and keep individuals with disabilities from
easily using electronic devices which could be detrimental in
a world which is becoming more reliant on electrical
devices.
IMPROVEMENT SOLUTIONS
Overcoming Flaws
The flaws discussed above provide software and
hardware developers a challenge to overcome language
interpretation barriers such as speech variations among
different users, innate language problems like homophones,
and user dependent systems. These problems are being
worked on by companies and individuals in order to
optimize the efficiency and accuracy of voice recognition
systems and improve technologies that are beneficial in
various practical applications. Solutions that are currently
being developed and implemented are software designs such
as the Neural Network Algorithm, Hidden Markov Model,
the MFCC algorithm, and combinations of multiple models
and algorithms. These solutions have beneficial applications
in areas such as security, convenience and accessibility for
the handicapped, emotion recognition, aiding scientific
research, and performing everyday tasks.
Modeling Speech
While speech recognition rates are low compared to a
technology like image recognition, research and
development of this software has of late grown steadily due
to growing expectations. The main challenge in designing
voice recognition software is not only analyzing frequency
and change in frequency of the voice, but also to attempt to
replicate with an algorithm how the brain recognizes
phonemes and syllables in speech. Two different main
models have been proposed and studied in order to extract
the speech signals from a voice and analyze their feature
parameters to have the computer recognize the speech input.
One model is based on a probabilistic approach algorithm,
while the other is an analytical algorithm. The Neural
Network (NN) recognition algorithm, mainly analytical, uses
a large coefficient matrix to match feature parameters of
syllables and words to an output index. This is not very
efficient. However, since every individual speech signal
must be run through the algorithm in order to be matched,
the algorithm must start from the beginning with every new
speech input. This is not ideal because of its slow rate of
recognition. A more probabilistic approach such as the
Hidden Markov Model (HMM) has proven to be faster with
recognition when faced with a larger number of speech
samples. The HMM attempts to improve recognition rates
by overcoming challenges such as rate of speech, ambient
noise, and varying voices by more successfully extracting
unique feature parameters of audio signals generated by the
voice [13].
Hidden Markov Model
The Hidden Markov Model selects quantitative
parameters in order to identify the unique features of speech.
An algorithm named the Mel-Frequency Cepstral
Coefficients (MFCC) algorithm is most commonly used to
identify specific parameters from a voice signal, which the
HMM uses to isolate further parameters. The MFCC is a
way of representing sound as a cepstrum (power spectrum
on a non-linear mel scale of frequency) by transforming the
cepstrum into a coefficient matrix. The matrix is a
representation of the syllable or word, which the HMM uses
to match to an initial database using various matrix
transformations to analyze based on a sequential
probabilistic model. As new voice and speech inputs are
transformed into MFCC matrices for different speakers and
syllables/words and inputted into the HMM, the HMM’s
initial probability is changed due to the variation of these
conditions. This transition probability is combined with the
initial probability based on the difference in number of states
and events in both transformations of MFCC matrices to
form a resulting observational probability. The three
probabilities (initial, transition, and observation) are then
used as variables to compare a test word or phrase to a pre-
trained test word or phrase. Improvements on these three

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variables are taken into account by the HMM when
comparing inputs. Applications of the HMM have been
tested, and research has found that when the number of
sample inputs increases, the recognition rate increases,
proving the HMM model to be successively more accurate.
A Mel-frequency index increase also slightly increases
accuracy, but slows processing time [13].
HMM ALGORITHM RECOGNITION RATE [13]
The rate of recognition of the HMM algorithm based on
various factors
MFCC Improvements
While the HMM focuses on interpreting user speech as
language, a solution that attempts to more accurately
recognize the speaker’s identity is a robust computer voice
recognition improvement of the MFCC algorithm based on
an individual user. This improvement tests a user’s voice
patterns and utilizes an improved MFCC algorithm to
produce a faster, more accurate result for voice recognition.
It attempts to achieve text-independent speaker
identification, or the identification of a user by their voice
without regards to the user’s specific language or words.
The modifications to the original MFCC include slightly
changing steps of the MFCC algorithm that convert the
sound into a MFCC matrix. After the speech signal is first
blocked into frames, it is windowed in order to minimize
error in signal discontinuities. Using the function of a
Kaiser window instead of a traditional Hamming window is
an improvement in that it minimizes the mean square error
instead of maximum error. A Fast Fourier Transformation is
used to convert the windowed frames from a time domain to
a frequency domain, which is then filtered using a Mel-scale,
which attempts to emulate the way the brain processes
different frequencies of speech as non-linear. Modifying the
way the Fast Fourier Transformation is applied over a Mel
filter has shown to reduce computing costs and make the
algorithm more efficient. Another suggested feature
matching technique (similar to HMM) is called Vector
Quantization. In this method, vectors assigned to MFCC are
mapped to a large area of space instead of matrices used in
HMM. Each small region within is referred to as a cluster,
with multiple centers of clusters (codewords) comprising a
codebook. To feature match, input MFCC vectors are
compared to clustered MFCC initial training vectors.
Research has shown these modifications to the MFCC
technique increase the speed of the algorithm (from 0.12 ms
to 0.11 ms) and increase the accuracy (from 66% to 80%) in
a sample of 50 tests [14].
Combining Methods
Various methods to obtain faster, more accurate speech
recognition, such as the ones previously detailed, all have
their own strengths and weaknesses. A solution proposed in
order to eliminate weaknesses in different approaches to
improving voice recognition and interpretation is to combine
multiple methods. One of these combinations is using the
HMM with Prediction by Partial Matching (PPM) method.
PPM is a technique that uses statistical analysis based on
context to predict the next character in a series. When
combined, a new system is created that uses the voice and
word recognition strengths of HMM with the prediction
matching of PPM to greatly improve input processing and
recognition [15]. Combinations of multiple proposed
solutions could be the key to the goal of increasingly
accurate voice recognition.
BENEFITS OF IMPROVEMING VOICE
RECOGNITION SYSTEMS
Practical Applications
Improved voice recognition technology is a further step
towards beneficial applications of the software in many
fields and aspects of life. These aspects can be applied in
consumer, corporate, industrial, and scientific fields in
various ways such as security, automation systems, ease of
use increases (convenience), and aiding other voice based
tasks. As the reliability and integration of this technology
increases, the more practical uses we will find will also
increase.
Security
One of the benefits provided by the improvement of
voice recognition software is the major application it has in
security. The potential for vocal based security systems
gives an added layer of protection in an era of information.
Speech recognition makes it possible to verify a person’s
identity through voice interfaces such as the phone and
computer microphone. This gives a person access to various
control and security actions such as controlling a mobile
device, voice banking, voice mail, voice activated security
control over certain devices, and remote access to a
computer [14]. Biological means of controlling computer
systems helps prevent fraud and theft based on an
individual’s unique physical characteristics [15].
Ease of Use and Handicap Accessibility

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Another of the various benefits of the applications of
voice recognition systems is the increase in ease of use for
controlling computer systems. This is probably one of the
most useful applications of the voice recognition software,
as it provides a means of control based on one of a person’s
most basic and instinctual processes: speech. Not only does
this make communication and control easier, it also gives
people with less access to controlling computers and even
basic machines a way to avoid having to physically control
systems. A home automation system was developed in order
to assist handicapped persons and the elderly with basic
household tasks they may be incapable of doing themselves.
Combining speech recognition software with wireless
networks allows the user to control most electronic devices
by voice, providing a 79.8% accuracy with 1225 commands
[16].
Sustainability: Looking Forward
As our use of technology grows and advances, more and
more emphasis is being placed on the sustainability of our
current systems and lifestyles, whether it is environmental or
societal. There is a growing awareness of the challenges we
must overcome in order to be able to sustain equilibrium
between meeting our present needs and ensuring that we
leave behind a world where future generations can live
comfortably. While a discussion of environmental impact
with regards to computer technologies and software would
be less meaningful than most other engineering topics, voice
recognition technology presents advantages that offer
flexibility in sustaining and improving quality of life. As
voice recognition technologies improve and unfold into new
applications, it is definitely a tool that could be used on
quality of life and sustainability enhancements.
Sustainability with regards to quality of life upgrades is
an important factor to consider when weighing the
importance of a technology such as voice recognition
software. Voice recognition software provides clear
improvements to quality of life. One example is the direct
applications to people with disabilities. Certain
functionalities of the technologies involved would provide
disabled people to become more independent. Less reliance
on other people creates a more enjoyable, free experience
where the disabled can find more meaningful interactions
and come closer to living a normal lifestyle. Even for
normal people in a home or work environment, less work
and effort has shown to reduce stress, increasing quality of
life where voice recognition technology cuts down on
manual labor.
Along with creating more significant experiences for
people due to its simplistic and intuitive nature, voice
recognition technology could also help improve quality of
life through its applications relevant to making the world a
safer place. Governments will have a better way to operate
smoothly and protect its citizens, through voice verification
systems and secure identity techniques. Even smaller
impacts such as hands-free control of a mobile device in a
motor vehicle could help reduce accidents and create safer
roads. The elimination of distractions due to the ability to
control electronics vocally would make the world safer and
improve quality of life.
CONCLUSION: FUTURE
DEVELOPMENTS OF VOICE
RECOGNITION SYSTEMS
In conclusion, voice recognition software, its
improvements, and technological applications have the
potential to positively affect the future of human society. Its
useful applications in security, productivity, and efficiency
provide concrete reasons why the development,
improvement, and integration of this technology is so
important. From consumer uses to government uses, voice
recognition is an important technology that has the potential
to benefit the lives of many people, and sustainably improve
the quality of life for future generations.
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ACKNOWLEDGMENTS
Thanks go to the Swanson School of Engineering, the
engineering professors and writing instructors, the library,
coffee, and the Internet.