speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
speech processing and recognition basic in data miningJimit Rupani
Basic presentation about speech processing
Name of the paper i read is :"An educational platform to demonstrate speech processing techniques on Android based smart phones and tablets" on Elsevier
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
speech processing and recognition basic in data miningJimit Rupani
Basic presentation about speech processing
Name of the paper i read is :"An educational platform to demonstrate speech processing techniques on Android based smart phones and tablets" on Elsevier
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
Wake-up-word speech recognition using GPS on smart phoneIJERA Editor
Wake-Up-Word (WUW) is a new prototype of speech recognition not widely recognized. Lately, the use of GPS is widely increased in everyday life that means that our necessities have changed. We can use a new paradigm in controlling the voice of a map in the digital era. This would bring benefit for people while driving a car. In this paper we present a set of voice commands to integrate within the map and navigation voice control. Using a voice control for Global Positioning System (GPS) helps to determine and track the precise location using a technology called Google API. The benefit of this application would be avoiding car accidents using speech command instead of typing.
In the realm of artificial intelligence (AI), speech recognition has emerged as a transformative technology, enabling machines to understand and interpret human speech with remarkable accuracy. At the heart of this technological revolution lies the availability and quality of speech recognition datasets, which serve as the building blocks for training robust yand efficient speech recognition models.
A speech recognition dataset is a curated collection of audio recordings paired with their corresponding transcriptions or labels. These datasets are essential for training machine learning models to recognize and comprehend spoken language across various accents, dialects, and environmental conditions. The quality and diversity of these datasets directly impact the performance and generalisation capabilities of speech recognition systems.
The importance of high-quality speech recognition datasets cannot be overstated. They facilitate the development of more accurate and robust speech recognition models by providing ample training data for machine learning algorithms. Moreover, they enable researchers and developers to address challenges such as speaker variability, background noise, and linguistic nuances, thus enhancing the overall performance of speech recognition systems.
One of the key challenges in building speech recognition datasets is the acquisition of diverse and representative audio data. This often involves recording a large number of speakers from different demographic backgrounds, geographic regions, and language proficiency levels. Additionally, the audio recordings must capture a wide range of speaking styles, contexts, and environmental conditions to ensure the robustness and versatility of the dataset.
Another crucial aspect of speech recognition datasets is the accuracy and consistency of the transcriptions or labels. Manual transcription of audio data is a labor-intensive process that requires linguistic expertise and meticulous attention to detail. To ensure the reliability of the dataset, transcriptions must be verified and validated by multiple annotators to minimise errors and inconsistencies.
The availability of open-source speech recognition datasets has played a significant role in advancing research and innovation in the field of AI speech technology. Projects such as the LibriSpeech dataset, CommonVoice dataset, and Google's Speech Commands dataset have provided researchers and developers with access to large-scale, annotated audio datasets, fostering collaboration and accelerating progress in speech recognition research.
Furthermore, initiatives aimed at crowdsourcing speech data, such as Mozilla's Common Voice project, have democratised the process of dataset creation by enabling volunteers from around the world to contribute their voice recordings. This approach not only helps to diversify the dataset but also empowers individuals to participate in the development of AI technologies that directly impact their lives.
Speech Recognition Application for the Speech Impaired using the Android-base...TELKOMNIKA JOURNAL
Those who are speech impaired (tunawicara in the Indonesian language) suffer from
abnormalities in their delivery (articulation) of the language as well their voice in normal speech, resulting
in difficulty in communicating verbally within their environment. Therefore, an application is required that
can help and facilitate conversations for communication. In this research, the authors have developed a
speech recognition application that can recognise speech of the speech impaired, and can translate into
text form with input in the form of sound detected on a smartphone. By using the Google Cloud Speech
Application Programming Interface (API), this allows converting audio to text, and it is also user-friendly to
use such APIs. The Google Cloud Speech API integrates with Google Cloud Storage for data storage.
Although research into speech recognition to text has been widely practiced, this research try to develop
speech recognition, specially for speech impaired's speech, as well as perform a likelihood calculation to
see the factor of tone, pronunciation, and speech speed in speech recognition. The test was conducted by
mentioning the digits 1 through 10. The experimental results showed that the recognition rate for the
speech impaired is about 80%, while the recognition rate for normal speech is 100%.
This is my take on what is going on in the world of mobile technology and how we can use it as Assistive Technology not just for people with disabilities but to makle everyone's life easier.
A Little More Conversation: Branding with Voice UILHBS
How we use and interact with machines and software is changing. Some assumed it was a fad, but we’re now seeing voice technology infiltrating our businesses, mobiles, vehicles and now our homes.
Brands are adopting the idea that voice is here to stay, which brings with it a shift in user experience, and in turn, customer expectations of usability.
Voice recognition systems enable consumers to interact with technology simply by speaking to it, enabling hands-free requests, reminders and other simple tasks.
For example:- ALEXA,SIRI
This presentation is a summary of our first event, it will give you a walk you through the technical capabilities of the major voice platforms (Amazon Alexa, Google Home, Siri, MS Cortana, Bixby etc), examine how they can be leveraged to build better products, and give an introduction to the voice-specific design process.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
2. History of speech
recognition:
1950s and 1960s: Baby Talk
The first speech recognition systems could understand only digits.
(Given the complexity of human language, it makes sense that
inventors and engineers first focused on numbers.) Bell
Laboratories designed in 1952 the "Audrey" system, which
recognized digits spoken by a single voice. Ten years later, IBM
demonstrated at the 1962 World's Fair its "Shoebox" machine,
which could understand 16 words spoken in English.
Labs in the United States, Japan, England, and the Soviet Union
developed other hardware dedicated to recognizing spoken
sounds, expanding speech recognition technology to support four
vowels and nine consonants.
They may not sound like much, but these first efforts were an
impressive start, especially when you consider how primitive
computers themselves were at the time
3. 1970S: SPEECH RECOGNITION
TAKES OFF
•Speech recognition technology made major strides in the 1970s, thanks to
interest and funding from the U.S. Department of Defense. The DoD's DARPA
Speech Understanding Research (SUR) program, from 1971 to 1976, was one of
the largest of its kind in the history of speech recognition, and among other
things it was responsible for Carnegie Mellon's "Harpy" speech-understanding
system.
• Harpy could understand 1011 words, approximately the vocabulary of an
average three-year-oldHarpy was significant because it introduced a more
efficient search approach, called beam search, to "prove the finite-state network
of possible sentences," according to Readings in Speech Recognition by Alex
Waibel and Kai-Fu Lee. (The story of speech recognition is very much tied to
advances in search methodology and technology, as Google's entrance into
speech recognition on mobile devices proved just a few years ago.)
•The '70s also marked a few other important milestones in speech recognition
technology, including the founding of the first commercial speech recognition
company, Threshold Technology, as well as Bell Laboratories' introduction of a
system that could interpret multiple people's voices.
4. 1980S: SPEECH RECOGNITION
TURNS TOWARD PREDICTION
Over the next decade, thanks to new approaches to understanding what
people say, speech recognition vocabulary jumped from about a few hundred
words to several thousand words, and had the potential to recognize an
unlimited number of words. One major reason was a new statistical method
known as the hidden Markov model.
Rather than simply using templates for words and looking for sound patterns,
HMM considered the probability of unknown sounds' being words. This
foundation would be in place for the next two decades (see Automatic Speech
Recognition—A Brief History of the Technology Development by B.H. Juang
and Lawrence R. Rabiner).
Equipped with this expanded vocabulary, speech recognition started to work
its way into commercial applications for business and specialized industry (for
instance, medical use). It even entered the home, in the form ofWorlds of
Wonder's Julie doll(1987), which children could train to respond to their voice.
("Finally, the doll that understands you.")
5. In 1990, Dragon launched the first consumer speech recognition
product,Dragon Dictate, for an incredible price of $9000. Seven years
later,the much-improved Dragon NaturallySpeaking arrived. The
applicationrecognized continuous speech, so you could speak, well,
naturally, atabout 100 words per minute. However, you had to train the
program for45 minutes, and it was still expensive at $695.
The advent of the first voice portal, VAL from BellSouth, was in
1996;VAL was a dial-in interactive voice recognition system that
wassupposed to give you information based on what you said on the
phone.VAL paved the way for all the inaccurate voice-activated menus
thatwould plague callers for the next 15 years and beyond.
6. 2000s: Speech Recognition Plateaus–Until Google Comes Along By 2001,
computer speech recognition had topped out at 80 percent accuracy,
and, near the end of the decade, the technology’s progress seemed to
be stalled. Recognition systems did well when the language universe
was limited–but they were still “guessing,” with the assistance of
statistical models, among similar-sounding words, and the known
language universe continued to grow as the Internet grew.
Did you know speech recognition and voice commands were built
into Windows Vista and Mac OS X? Manycomputer users weren’t aware
that those features existed. WindowsSpeech Recognition and OS X’s
voice commands were interesting, but notas accurate or as easy to use
as a plain old keyboard and mouse.
7. In 2010, Google added “personalized recognition” to Voice Search
on Android phones, so that thesoftware could record users’ voice searches
and produce a more accuratespeech model. The company also added
Voice Search to its Chrome browserin mid-2011. Remember how we
started with 10 to 100 words, and thengraduated to a few thousand?
Google’s English Voice Search system nowincorporates 230 billion words
from actual user queries.
And now along comes Siri. Like Google’s Voice Search, Siri relies oncloud-based
processing. It draws what it knows about you to generate
acontextual reply, and it responds to your voice input with personality.(As
my PCWorld colleague David Daw points out: “It’s not just fun butfunny.
When you ask Siri the meaning of life, it tells you ’42’ or ‘Allevidence to
date points to chocolate.’ If you tell it you want to hidea body, it helpfully
volunteers nearby dumps and metal foundries.”)
Speech recognition has gone from utility to entertainment. The
childseems all grown up.
8. THE FUTURE
Accurate, Ubiquitous Speech
The explosion of voice recognition apps indicates that
speechrecognition’s time has come, and that you can expect plenty
more appsin the future. These apps will not only let you control your PC
byvoice or convert voice to text–they’ll also support multiplelanguages,
offer assorted speaker voices for you to choose from, andintegrate into
every part of your mobile devices (that is, they’llovercome Siri’s
shortcomings).
The quality of speech recognition apps will improve, too. For
instance,Sensory’sTrulyhandsfreeVoice Control can hear and
understand you,even in noisy environments.
9. WHAT IS SPEECH RECOGNITION??
Speech recognition is the ability of a machine or program to identify
words and phrases in spoken language and convert them to a machine-readable
format.
Another definition
Speech recognition is an alternative to typing on a keyboard. Put
simply, you talk to the computer ,mobiles and your words appear on the
screen. The software has been developed to provide a fast method of
writing on a computer and can help people with a variety of disabilities.
It is useful for people with physical disabilities who often find typing
difficult, painful or impossible. Voice-recognition software can also help
those with spelling difficulties, including users with dyslexia, because
recognized words are almost always correctly spelled.
10. However, speech is more than sequences of phones that forms words
and
sentences. There are contents of speech that carries information, e.g.
the
prosody of the speech indicates grammatical structures, and the stress
of a
word signals its importance/topicality. This information is sometimes
called
the paralinguistic content of speech
11. Advantages
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!
Disadvantages
Even the best speech recognition systems
sometimes make errors. If there is noise or some
other sound in the room (e.g. the television or a
kettle boiling), the number of errors will increase.
Speech Recognition works best if the
microphone is close to the user (e.g. in a phone,
or if the user is wearing a microphone). More
distant microphones (e.g. on a table or wall) will
tend to increase the number of errors.
12. Voice recognition software
Voice-recognition software programs work by analyzing
sounds and converting them to text. They also use
knowledge of how English is usually spoken to decide
what the speaker most probably said. Once correctly set
up, the systems should recognize around 95% of what is
said if you speak clearly.
13. Voice recognition in
operating systems
Mobile Devices / Smart phones
Many cell phone handsets have basic dial-by-voice
features built in. Smartphones such as
iPhone or Blackberry also support this. A
number of 3rd party Apps have implemented
natural language speech recognition support,
including:
14.
15. Smart phones and mobile devices are in the middle
of major innovations in technology to provide
hands-free access to features and navigation, often
called voice commands, voice-enabled, voice
actions or speech recognition. This technology has
major implications for use by people who have
disabilities as assistive technology. As long as a user
has a strong, clear voice, these devices become
easier to use and give increased access to use of the
Internet, use of mobile devices and communication
accessibility.
16. Windows 7 built-in speech recognition
The Windows Speech Recognition by Microsoft is the speech recognition
system that comes built into Windows Vista andWindows 7. Windows
Vista and Windows 7 include version 8.0 of the Microsoft speech recognition
engine. Speech Recognition is available only in English, French, Spanish,
German, Japanese, Simplified Chinese, and Traditional Chinese and only in
the corresponding version of Windows. That means that you can not use the
French speech recognition engine if you use an English version of Windows.
Windows XP or 2000 only
e-Speaking – software for Windows XP that facilitates use of
the Microsoft Speech API by adding ability to create commands to perform
custom actions.
Microsoft Speech API – Speech recognition functionality included as part of
Microsoft Office and onTablet PCs running Microsoft Windows XP Tablet PC
Edition. It can also be downloaded as part of the Speech SDK 5.1 for
Windows applications, but since that is aimed at developers building speech
applications, the pure SDK form lacks any user interface, and thus is
unsuitable for end users.
Vestec Inc. - Specializing in Natural Language Understanding and Speech
Recognition solutions. ASR, NLU and TTS engines support 17 languages in
server, embedded (on low cost chip) or cloud based environments.
18. Types of speech recognition
1. Text-To-Speech:
As it sounds, Text-To-Speech (or TTS) will
manipulate a string of text into an audio clip.
It is useful for blind people to be able to use
computers but can also be used to simply
improve computer experience. There are
several programs available that perform TTS,
some of which are command-line based
(ideal for scripting) and others which provide
a handy GUI.
19. 2. Simple Voice Control/Commands:
This is the most basic form of Speech-To-Text
application. These are designed to recognize
a small number of specific, typically one-word
commands and then perform an action. This
is often used as an alternative to an
application launcher, allowing the user for
instance to say the word “firefox” and have
his OS open a new browser window.
20. 3.Full dictation/recognition:
Full dictation/recognition software allows the
user to read full sentences or paragraphs and
translates that data into text on the fly. This
could be used, for instance, to dictate an
entire letter into the window of an email
client. In some cases, these types of
applications need to be trained to your voice
and can improve in accuracy the more they
are used