TranscribeMe creates structured data sets for customers to use to create or enhance machine learning models.

Before getting to case studies illustrating this work, some terms need to be either defined or clarified, i.e., “structured data” and “AI.”

I consider AI to be a misnomer. Intelligence is intelligence; excluding all other flora and fauna, it divides into human or machine. So for me, there’s nothing artificial about an intelligent machine. It’s simply not human.

Learning Through Structured Data

Consider how humans learn. A newborn is pretty much helpless, but from birth it packs an enormously powerful and complex brain that from day one is collecting, integrating, and assimilating environmental data, including speech. Without speech, the child is in stealth mode, but the right brain is hyper engaged in an activity that data scientists would call unsupervised learning.

As the child grows, structured data is introduced in the form of books. Initially, a parent may read to the child and point out elements in the story. For example, while reading “Goodnight Moon,” the parent might say, “Moon,” then point to its picture, tying the word to a visual. That is data annotation!

As children continue to learn, the enormous capacity of the brain to log, store, and collate data comes into play and the children become, for the most part, autonomous learners.

A newborn machine has neither a right brain, nor the nearly unlimited data capacity of a human brain to begin learning and storing data. It’s estimated that a human brain can store 2.5 petabytes of information. That would be equivalent to a DVR recording continuously for 300 years!

A newborn machine begins its quest for intelligence at the Goodnight Moon stage where a pairing takes place: an audio recording of the word “moon” with the written word, or an image of the moon with an audio recording of the word. 

As is the case with the child learner, this is data annotation.

An example of structured data could be, let’s say, a complex set of data defining all North American songbirds at the exclusion of all else. This would produce an intelligent machine that could identify every single songbird on the continent. But it couldn’t tell us a thing about butterflies! And there would be nothing in its database or algorithmic logic to take it from songbird to butterfly. 

A new set of structured data must be created and assimilated for every new thing we want our machine to learn. It’s always been this way from the beginning of time, machine learning time, that is.

Here’s a quote from Wikipedia in the article, Expert System: “In the late 1950s… biomedical researchers started creating computer-aided systems for diagnostic applications in medicine and biology. These early diagnostic systems used patients’ symptoms and laboratory test results as inputs to generate a diagnostic outcome.” Even for the first machines, data annotation was required.

From the 1950s until now, all machine learning has required data annotation to create structured datasets to create or enhance machine learning models. There have been many claims of unsupervised learning, but that has not been true in cases we’ve seen. The machines have gotten more sophisticated with their data collection, but overall the machine needs to be trained for a specific use.

Use Cases for Annotated Data

Every day AI and machine learning technologies are delivering astounding accomplishments that benefit a broad spectrum of fields and people around the world, including encompassing areas such as software and development, cybersecurity, medicine, engineering, customer service, finance, manufacturing, and more.

But scientists, technologists, and huge industries are not the only ones reaping the benefits of machine learning. Small businesses and individuals alike are beginning to understand that data collection and analysis are now the norm, so it is no wonder that AI and machine learning are among the fastest growing technologies globally.

These technologies include audio, images, videos, podcasts and more. Simply put, data is labeled to make it comprehensible to AIs. The key is the accuracy of the data sets and the quantity of data sets is also very important so that there is increased variety in the verbiage and context. 

This is where TranscribeMe comes in. We have been asked to provide annotated data for a variety of use cases. And we have teams that are specially trained to label and process data appropriately for any given project. Here are just a few examples:

Medical Services

Topic: Medical Emergency Screening
Form of Data Annotation: Audio
Process: Annotators listen to agonal breathing recordings and mark the beginnings and ends of the wavelengths.
Purpose: To be able to teach the provider’s automated system to screen patient calls for agonal breathing in order to identify callers who are experiencing a heart attack or stroke.

Fast Food Industry

Topic: Accuracy of Automated Orders
Form of Data Annotation: Audio/text
Process: Customers’ drive-thru orders are transcribed.
Purpose: To train the restaurant’s automated system to recognize drive-thru orders that are placed by learning to recognize menu items regardless of customers’ accents and despite high levels of surrounding noise.

Telephony Company

Topic: Customer Service Analysis
Form of Data Annotation: Text
Process: Specific labels are used to tag words or phrases in pre-transcribed customer service conversations.
Purpose: To build custom speech models for call center use cases by identifying customer sentiment, logging why customers call, as well as how the calls end, and by qualifying the agents’ responses.

Court Stenography Company

Topic: Annotation via Keywords
Form of Data Annotation: keyword spotting
Process: Words and phrases from notices of depositions are tagged according to keywords per the clients’ instructions.
Purpose: To compile data sets from deposition notices using keywords that identify plaintiffs, defendants, witnesses, attorneys, deposition location, date, time, and other similar information.

Self-Driving Vehicle Manufacturer

Topic: Passenger Safety
Form of Data Annotation: image tagging
Process: Annotators use special software to draw a shape around specific images in photos and videos.
Purpose: Tagged images are used to teach self-driving vehicles to avoid obstacles in the road such as potholes, cracks, water, etc.

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Artificial intelligence (AI) is a rapidly evolving field that has the potential to transform numerous industries and improve our daily lives. However, building an effective AI system requires the use of high-quality training data. In this blog post, we will explore what AI training data is and why it is essential for AI development.

What is AI Training Data?

AI training data is a set of labeled examples that is used to train machine learning models. The data can take various forms, such as images, audio, text, or structured data, and each example is associated with an output label or annotation that describes what the data represents or how it should be classified.

Training data is used to teach machine learning algorithms to recognize patterns and make predictions. By feeding a large amount of data with known labels into a machine learning algorithm, the algorithm can learn to recognize patterns and make predictions about new, unseen data.

Why is AI Training Data Important?

The quality and quantity of training data sets are crucial to the accuracy and effectiveness of machine learning models. The more diverse and representative the data is, the better the model can generalize and perform on new, unseen data. Conversely, biased or incomplete training data can result in inaccurate or unfair predictions.

For example, imagine the AI system is trained to recognize human voices but only on data from a single gender or accent. Such a system is likely to perform poorly on folks from other regions or have different accents. This is why it is crucial to carefully select and preprocess training data, ensuring that it represents the target population and is labeled accurately and consistently.

Additionally, training data can help mitigate the risk of AI bias. Bias in AI can occur when the training data is not representative of the target population or when the labeling process is biased. This can lead to unfair or discriminatory predictions, such as denying loans or job opportunities based on factors like race or gender.

By ensuring that the training dataset is diverse and representative and by using unbiased labeling processes, we can reduce the risk of AI bias and ensure that AI systems are fair and accurate.

What Are the Three Types of AI Training Data?

The three types of AI training data are:

1

Supervised learning datasets

Supervised learning is the most common type of machine learning, and it requires labeled data. In supervised learning, the training data consists of input data, such as images or text, and associated output labels or annotations that describe what the data represents or how it should be classified.

2

Unsupervised learning datasets

Unsupervised learning is a type of machine learning where the data is not labeled. Instead, the algorithm is left to find patterns and relationships in the data on its own. Unsupervised learning algorithms are often used for clustering, anomaly detection, or dimensionality reduction.

3

Reinforcement learning datasets

Reinforcement learning is a type of machine learning where an agent learns to make decisions based on feedback from its environment. The training data consists of the agent's interactions with the environment, such as rewards or penalties for specific actions.

Benefits of High-Quality AI Training Datasets

There are quite a few benefits of high-quality AI training datasets:

Improved accuracy and reliability

High-quality training data can improve the accuracy of machine learning models. When a model is trained on diverse, representative, and accurate data, it can better recognize patterns and make more accurate predictions on new, unseen data.

Faster model training time & development

High-quality training data can accelerate the development of machine learning models. With access to high-quality data, developers can quickly iterate and improve their models, reducing the time and resources required for development.

Better generalization

High-quality training data can improve the generalization ability of machine learning models. When a model is trained on diverse data, it can better adapt to new, unseen situations and perform well in real-world scenarios.

Reduced bias

High-quality training data can help reduce bias in machine learning models. By ensuring that the training data is diverse and representative, and by using unbiased labeling processes, we can reduce the risk of AI bias and ensure that AI systems are fair and accurate.

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In a 2021 report, Algorithmia discovered that companies are actively broadening the scope of what they are using AI and machine learning to accomplish, with a clear focus on the automation of business processes and customer experience. 

So if you run a business, does this mean you should hurry to allocate some money in next year’s budget for a data scientist or two, maybe even start a new ML division in your IT department? Sure, you can. And maybe it will even work great for you. But like everything in life, it’s often not as simple as that.

AI/ML Implementation Obstacles

Nearly all companies agree that AI and ML can be beneficial to their businesses if implemented successfully, but it is the successful implementation aspect that has its share of issues.

In May of 2019, Dimensional Research conducted a global survey of data scientists, AI experts, and stakeholders in large companies across 20 industries to determine their experiences with machine learning development projects. The report, Artificial Intelligence and Machine Learning Projects Are Obstructed by Data Issues, is loaded with interesting information. We highly recommend reading it all, but here are a few key takeaways:

• 78% of AI/ML projects stall at some stage before deployment
• 96% of enterprises encounter data quality and labeling challenges
• 63% have tried to build their own technology solutions
• 81% admit that training AI with data is more difficult than expected

The most common hurdle organizations face when deploying AI/ML solutions is around the data used to build and train the models. Companies frequently misunderstand or underestimate the data they already have and the utility it provides, how that data needs to be organized & labeled, and what data is needed to be acquired to properly build the required models. As a result, the projects cannot be implemented or are implemented poorly due to various data quality-related issues.

Most enterprises that do have machine learning teams in place, even very large organizations,  have 10 or fewer people working on their AI/ML teams. 24% have fewer than 5. To deploy a machine learning model at the production level with confidence, a majority of ML projects require hundreds of thousands (if not millions) of labeled data items. These data science teams are not able to properly label all of the required training data in-house, and they often outsource these tasks to outside vendors, trying to receive at times complex annotations using outsourced lowest-cost labor. Often times this results in labeled data not achieving the accuracy needed to properly train the models, which results in a negative feedback loop – feeding bad data into an ML model results in poor model performance, which results in feeding more bad data into the model resulting in continued poor performance.  

The research report Reshaping Business with Artificial Intelligence, published in the fall 2017 issue of the MIT Sloan Management Review, reveals another issue that stands out. The report shows that there is a lack of understanding by policy-makers and strategists regarding certain aspects of ML, particularly when it comes to the comprehension that generating business value from AI is directly linked with training AI algorithms:

“Many current AI applications start with one or more ‘naked’ algorithms that become intelligent only upon being trained (predominantly on company-specific data). Successful training depends on having well-developed information systems that can pull together relevant training data.”

In-House or Outsource Machine Learning Projects?

After reading about the difficulties companies may face in attempting to launch a successful machine learning project, it should be no surprise to hear that, as revealed by the Dimensional Research survey, 71% of organizations ultimately outsource ML project activities. Moreover, teams that outsource data labeling get projects into production faster. 

And of that 71% of organizations that outsourced ML activities, three of the top five external services utilized were related to training data. 

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TranscribeMe uses automatic speech recognition (ASR) technology in order to auto-complete audio to text transcripts.

When the audio is of very high quality and the completion requirements are less than 100% word accuracy, an ASR can provide a pretty accurate finished transcript in a short amount of time. This accuracy is usually accomplished when there’s either a single speaker or where there’s a dialogue between two speakers each with a separate mic–which could be two phones. Though you would think that this would be the best way to create data sets for more accurate ASR, this is not always the typical case for several reasons, the primary one being audio quality.

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Today’s fast-paced lifestyle, combined with a growing preference for finding simplified ways of completing daily tasks and responsibilities, has led to the proliferation of the use of speech recognition.

Indeed, since Google’s introduction of voice search in 2011, which was then considered a novelty, the feature is now one that users regularly rely on. What’s more, improvements in speech recognition technology have transformed voice search into a key component of search marketing.

Within the realm of artificial intelligence, voice recognition is completely transforming the way that we interact with technology. And now, with the availability of smart-home voice assistants (Alexa, Google Assistant and Siri) that undergo regular updates to their software which continue to improve their intuitiveness and intelligence, voice recognition is a part of regular daily life for many.

The accuracy and complexity of speech recognition technology makes one wonder, what is really going on under the hood? How does speech recognition work? Below we delve deeper into understanding it.

How does it all work?

Speech recognition technology comes in a few forms; in some cases, it serves as an alternative to typing on a keyboard; words appear on a screen by way of talking to the computer thanks to software that analyzes the audio of a speech recording using algorithms to accurately match the individual sounds to written language.

In other cases, speech recognition technology translates audio algorithmically into a certain action that is then performed by another piece of technology — as is the case with smart-home assistants, which translate users’ speech to commands like turning smart devices on our off, or changing the song that is currently playing.

Whatever the end goal might be, speech recognition technology works very similarly in the aforementioned situations; an audio message — whether on your phone or desktop — is transcribed on the server. The bits of data from the audio message are sent to a central server, where it can access the appropriate software and corresponding database. Here, the server analyzes the audio and breaks down the speech into smaller, recognizable parts called phonemes. From here, it’s the phonemes that enable audio analysis software to figure out exactly what is being said. In the case of words that are pronounced similarly, the software is able to analyze the context of the audio and syntax of the sentence to identify the best text match for the words within the audio file.

Finally, this analysis results in a written transcription of the data or in a secondary action in the form of instructions to be undertaken by another piece of technology (like a smart-home device, for example).

At TranscribeMe, we use our Machine Express service for speech-to-text transcription. It employs the most advanced automated speech recognition algorithms to create the highest accuracy automated transcriptions on the market.

If you would like to learn more about the product or have bulk or custom requirements, contact our Sales Team today!

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Long gone are the days of manually clocking in and clocking out at the office. With the arrival of computers and the internet, the digitalization of how we work seems as though it happened overnight. Game-changing technology showed up at our doorstep with the first mass-marketed desktop computer with keyboards for typing in the late 60s and has been on a consistent marathon of progress ever since. 

Perhaps you even remember the enthusiasm with which typing was met? 

Right from the days of typewriters, the skill of typing already held great value for a number of professions. So much so that speed typing competitions have been around for decades with presence on an international scale. The tapping of number and letter keys became the easiest way to translate human speech into text.

Nowadays, with the shift towards hand-held devices, the typing skill set has been adapted to this current more popular use. However, with the rise of speech recognition technology, typing is beginning to take a backseat to voice command. While still not yet 100% accurate, voice recognition technology is experiencing an exponential uptake by users around the world.

What’s on the horizon for 2030?

For starters, global internet usage is forecast to reach 80% of the world’s population, up 23% from today. This expected surge in online users signals great market growth for all kinds of smart devices, including home devices such as Google Home and Amazon’s Echo. What’s more, Gartner predicts that by 2020, 30% of web searches will be performed without a screen. A decade later, we can imagine how much less relevant and in-demand keyboards and keypads will be.

Artificial Intelligence has already made great progress in recent years and is projected to do so at an even faster rate. The technology is forecast to move from basic level functionality to more complex decision-making capabilities. In this way, the requirement for input from humans as users will not be as high as it is today. Advanced AI systems will be robust enough to make predictions around user behavior and sign off on any pre-approved decision making. With the Internet of Things on the rise, an example of this could be your fridge ordering more milk and eggs on your behalf once it detects that they will be finished before your next food shop.

What’s more, technology is increasingly moving in the direction of being wearable, which means its cues to respond will crossover into the world of biometrics and external stimuli. Imagine a smart t-shirt that loosens and tightens its fibers to control airflow according to your body temperature. You’ll no longer have to type to find out the weather forecast before deciding what clothing to wear. Intelligent apparel will be online and interconnected with access to a wealth of data from which to draw sound conclusions without much input from us, if at all.

Where does all this leave transcription?

While it’s plain to see that typing is increasingly set to lose its relevance, it doesn’t signify the end of transcription. It simply means a new era is upon the profession that relies more on speech and voice recognition technology. 

At TranscribeMe, we already work with state-of-the-art AI modelling to produce highly accurate transcriptions using automatic speech recognition. Our hybrid transcription service combines computer-generated transcripts with the quality assurance of skilled transcriptionists to guarantee full verbatim style. Due to nuances in speech and dialect that are not always picked up by machines, discrepancies that require human editing using a computer screen and keyboard.

However, as technology advances in this space, the need for human modification via typing in transcription will quickly disappear. Given the current technological landscape, we can easily imagine that a combination of speech and simple hand gestures (or even eye movements) will be all that transcriptionists need to edit automated transcriptions. This will replace the need for bulkier, redundant equipment such as keyboards or keypads. That said, no one can be sure exactly what can be expected by 2030 as a number of factors influence progress, but time will surely tell.

Want to make the most of the very latest in speech-to-text technology for accurate and affordable machine transcription with our express delivery? Contact us for more information on our best-in-class services that keep raising the bar.

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With Artificial Intelligence (AI) increasingly becoming a staple in our day-to-day lives, confusion around the correct use of the related lingo is very common. This is especially the case in conversations between non-experts and implies some people could be vulnerable to marketing ploys that take advantage of the misuse of this terminology. 

One particular example is the difference between speech recognition and voice recognition, which are often used interchangeably. Are the two terms similar? Yes, but do they refer to and mean the same thing? Not at all! Read on for the key differences in their definition and application so you can use the terms with confidence.

Speech Recognition for The Masses

The purpose of speech recognition is for a computer or machine to successfully identify the words spoken by absolutely anyone. With this method, there is no need to pay attention to more personal details such as accent, cadence, and the like.

The main goal with this technology is to achieve maximum accuracy and speed with speech recognition, well surpassing even the highest capability of humans. Automation of this process has the potential to save an incredible amount of valuable time that can be channeled into other more productive activities.

At present, speech recognition technology has not yet achieved 100% accuracy, despite having been around since the late 50s. While current accuracy rates can be as high as 98%, the main obstacle to achieving complete precision is the high variation that exists in human speech. Everyone has their own unique style of speech, including accent, pronunciation, and enunciation. 

Voice Recognition for Personalization

On the other hand, voice recognition is about being able to identify and understand one specific voice. The most widespread use of this technology is with virtual assistants such as Apple’s Siri or Amazon’s Alexa. In fact, it has been predicted that 75% of US households will own and use at least one smart speaker by 2020.

The main goal of voice recognition technology is to enable voice command features. The first step of correctly recognizing the speaker acts as a secure identification process. This is particularly important when the authorization of payments is required and as such, acts as a biometric security measure.

For example, imagine you ask your phone or smart home device to look into train times for a specific time, date and journey. Accurate identity verification based on voice recognition would be necessary to then book the train ticket of choice.

How the Two Technologies Apply to Transcription 

While we’ve made the distinction between voice and speech technology, the common ground between the two is that they both involve the conversion of audio to text. Seeing as this is exactly what transcription is all about, it’s clear to see the connection both technologies have to the service.

Voice recognition utilizes the input of text derived from one specific speaker to follow their command and perform an exact function. Speech recognition is applied more directly to transcription services as a way of automating the generation of transcripts, yet the uses of this digitized output are numerous. What’s more, speech recognition allows for the identification of multiple speakers, unlike voice recognition.

At TranscribeMe, we pride ourselves on our best-in-class online transcription services using the latest technology in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP). We work with a large community of highly-skilled transcriptionists and exclusively use top quality data sets to train our engines through machine learning. Thanks to this, we are able to offer transcript accuracy starting at 98% in a number of languages and dialects.

Interested to know more about what our ASR can do for your business? Get in touch with our sales team today to request a demo and further information.

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Speech recognition refers to the computational ability to convert spoken language into text. Speaking is our most natural way of communicating, and it is also our fastest way to do so. While increased speed is the main reason voice command is rapidly becoming a main feature on smart devices, it’s also a matter of convenience. Practicality has long been known to boost user uptake, especially in relation to technology.

That said, speech recognition technology is still not perfect due to the infinite diversity in speaking style. Despite this branch of Artificial Intelligence (AI) having been around since the late 1950s, the high level of nuance in human speech is keeping computers from obtaining complete accuracy.

Natural Language Processing (NLP) is the field within AI that is tackling this challenge by developing effective applications that allow for the interaction with machines using natural language. The research of this ever-expanding field is key to improving speech recognition and its training as a means to increase the precision we can achieve with the technology. Speech recognition training has a beneficial effect on machine learning.

Speech Recognition Training Feeds Machine Learning

As with most AI streams, training a smart technology involves feeding the software with relevant high quality datasets. The more datasets a machine runs through, the greater experience it gains and the stronger the algorithm it builds. In terms of speech, the more practice a computer can get, the faster it will get at processing data due to its familiarity with the type of data.

However, accuracy is not something machines can improve without any human input or the use of quality datasets. This is where annotated data comes in. Making the most of the linguistic data online generated by the billions of internet users today means channelling it into machine learning tasks. Our scope would be limited to much smaller amounts of data and an even longer turnaround time if we were left to do this work manually.

Annotated Data In a Nutshell

Annotating data is the act of labeling digital information in a form that can be indexed when processed by machine learning. These labels provide further value to the significance of data by adding attribute tags. Given that data annotation is mainly overseen by human analysts, great accuracy is required during this tagging process to ensure that the computer algorithms learn as effectively as they can efficiently. Relevancy to the task at hand is another important factor in achieving the desired outcome so as to avoid any irrelevant data patterns from being learnt.

The Main Types of Data Annotation

The application of annotated data is increasingly versatile which implies that its uses are also expanding. Here are some of the different types of data annotation:

• Semantic annotation – This involves the identification of various concepts such as names or objects within text files to create references upon which an algorithm will learn from.
• Video/image annotation – Here, image recognition is used to help identify different content of interest, in a still or moving context.
• Text/content categorization – This assigning of attributes helps classify written content according to predefined categories
• Entity annotation – In order to make information machine-readable, this process labels unstructured sentences.

How We Apply Data Annotation to Machine Learning

At TranscribeMe, we make the most of our powerful human transcription and human-assisted speech data to train speech recognition engines. This has resulted in the creation of EVA by Voicea – a smart AI virtual assistant for the workplace that helps ensure every important detail and action item is taken note of. With expertly-trained speech recognition systems, voice-based assistance programs like EVA can solve problems faster and take your business productivity to the next level.

We are specialists in transforming large volumes of speech data into client-specific corpora which are consequently used to train AI systems and Automatic Speech Recognition (ASR) platforms. At present, we offer these services in all English accents, Spanish (European & Latin American), Portuguese, Mandarin, Cantonese, Japanese, French and Italian.

We pride ourselves on our ability to deliver highly accurate, human-verified transcription services. The output of these services are further applied to high quality speech recognition training that has a wide range of use cases. With each file we transcribe, our automated speech recognition models improve further. Our robust platform generates better results each time it learns something new. We offer fully-customized AI model training for your speech recognition systems, which includes:

• Custom annotations
• Complete full verbatim transcription
• A multiple step review process
• Capabilities to include customized meta-tags
• Multiple language supportAnd much more!

Interested to know more about how this training service can best be put to use according to your enterprise needs? Request a demo today!

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Speech recognition technology has come a long way since its debut on the mainstream market in the 1980s. It wasn’t long before the technology had advanced to respond to voice commands, and virtual assistance was invented. The rise in the use of voice search has generated vast amounts of speech data. Thanks to this, speech recognition training has been able to gain significant momentum.

However, the main barrier to further advancements is the ongoing challenge of understanding human speech with sufficient accuracy. As fast as computers have been able to learn with more robust machine learning models, the unique and diverse nature of how we speak as individuals has been a force to be reckoned with. Natural Language Processing (NLP) is the field that is working on cracking the code to the universal understanding of human speech.

Understanding the context in which words are said is the current missing piece to a seamless experience using virtual assistance.

The Birth of The Virtual Assistant

Remember when the virtual assistant Siri came on the market with Apple’s iPhone in 2011? At first, consumers were excited by this novel feature on their mobile devices and even impressed by Siri’s sharp, humorous responses to certain question cues. At this point, Siri could only perform simple functions like initiating a call or running an online search at request. But it wasn’t long before Siri began to disappoint with a reduced ability to decipher voice commands in a noisy environment.

In fact, the funny ways in which users have been misunderstood by Siri and other virtual assistants has been a trending topic on the internet. Despite these hiccups in user experience, other mobile phone manufacturers quickly followed suit by adding speech recognition search engines to their devices due to the promising potential the technology held. More recent advancements in integrations with other apps have now increased the complexity of commands that can be carried out.

Access to Data is What Gets You Ahead of The Game

Naturally, after Siri’s launch, other tech giants such as IBM, Google, Microsoft and Amazon began to unveil their virtual assistant technologies. Each company focused on the unique strengths their products provided for their target users. Amazon joined the race by bringing their smart home devices, Echo and Alexa, onto the market; while IBM’s supercomputer Watson was geared towards businesses and Microsoft’s Cortana was integrated through Windows 10.

In terms of accuracy, Google has the biggest advantage due to its search engine data that serves as the basis for its speech recognition training. Amazon is quickly catching up with a majority share of the household smart device market. Data equates to real-life experience, which machine learning tools can process and use to build a more efficient pattern of speech recognition.

The Benefits of Virtual Assistance in The Workplace

The global market for speech recognition software is predicted to grow at a constant annual growth rate of 12% in the coming years. The use of voice command in online search is also forecast to increase. In the home, virtual assistance is being targeted at household appliances that are being integrated with the Internet of Things. In the workplace, virtual assistance has become of growing interest for businesses due to its ability to optimize workflow.

To improve the performance of virtual assistants, access to large amounts of good quality data is a must. At TranscribeMe, we work with Voicea to train speech recognition engines with human-assisted speech and transcription data. Through this collaboration, the intelligent AI virtual assistant EVA was created. EVA is programmed to carry out actions on command, capturing important action items and other details in office meetings, whether online or in person. Voice-based assistance programs like this can help address problems in the workplace faster and more efficiently, creating a more productive office.

Speech recognition accuracy is expected to improve at an even faster rate than ever, thanks to a growing shift in user behavior to more voice-led, screenless interactions. This implies that virtual assistants will also reap the benefits of such improvements to offer more services to their users. With our global network of thousands of voice-to-text experts, we offer best-in-class human-assisted machine learning transcription services. This is how we help contribute to enhancing the accuracy of speech recognition engines which serve as the basis for virtual assistance technology.

Ready to learn more about how to improve your machine learning and AI systems through speech recognition? Get in touch with our team today to request a demo of our Automated Speech Recognition!

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At TranscribeMe, we offer Speech Recognition using state-of-the-art technologies and expertise to provide businesses with the highest accuracy automated transcriptions. Speech recognition refers to the technology by which a machine or program is engineered to identify spoken words or phrases and convert them to text or any other format that can be read by a machine. Here are some quotes from experts that explain how this in-demand technology actually works, some of its many applications and challenges the field currently faces:

How Does Speech Recognition Work?

“The computer takes in the waveform of your speech. Then it breaks that up into words, which it does by looking at the micro pauses you take in between words as you talk.”

– Meredith Broussard, Data Journalist and Professor at NYU

“Let’s say we have a particular speech sound, like the word “one.” If I have a couple thousand examples of a one I can compute the statistics of its acoustic properties, and the more data — the more samples of one — I have the more precise the description become. And once I have that I can build fairly powerful recognition systems.”

– Alexander Rudnicky, Research Professor with the Carnegie Mellon Speech Group

“So the lexical models are built by stringing together acoustic models, the language model is built by stringing together word models, and it all gets compiled into one enormous representation of spoken English, let’s say, and that becomes the model that gets learned from data, and that recognizes or searches when some acoustics come in and it needs to find out what’s my best guess at what just got said.”

– Mike Cohen, Manager of Speech Technologies at Google.

“Let’s start with speech recognition. Before we go and train a speech system, what we have to do is collect a whole bunch of audio clips, so for example, if we wanted to build a new voice search engine, I would need to get lots of examples of people speaking to me, giving me little voice queries. And then I would actually need human annotators or I need some kind of system that can give me ground truth, it can tell me for a given audio clip, what was the correct transcription. And so once you’ve done that, you can ask a deep learning algorithm to learn the function that predicts the correct text transcript from the audio clip.“

– Adam Coates, Director of Baidu’s Silicon Valley AI Lab.

What Are Some of the Ways It Can Be Applied?

“From a person’s voice alone most people can tell if someone is angry or nervous, but there are a ton of subtle things that are not perceivable by the human ear that are also connected to your thoughts. In our work, we measure thousands of aspects of speech and language, and many of them go beyond human hearing. We certainly can’t objectively measure them, but machines can, and those features are often highly correlated with one’s cognitive status and can indicate whether someone has Alzheimer’s, dementia, depression or anxiety.”

– Dr. Frank Rudzicz, Toronto Rehabilitation Institute-UHN

“In the past decade, voice based solutions were mostly used in banking and telecom call centers as well as in healthcare, but this was largely an experimentation stage, considering the issues of accuracy and business relevance. Only in the past few years, we noted a significant increase in demand and preparedness for speech technologies in financial services, insurance, and other sectors. There are many positive implementation examples across these industries: e.g. Barclays, Citibank, ING, Wells Fargo and others in banking.”

– Alexey Popov, CEO at Spitch

“It’s nice that ASR [Automatic Speech Recognition] is actually starting to be useful now. When I started out, the most visible ASR product was Dragon Dictate, which few people actually used— I believe it was marketed as the ideal Christmas present, which was deceptive. These days we have Amazon Alexa and Google Home, which people actually use — not to mention call center dialog systems. They are annoying, but that’s often a limitation from the dialog management rather than the ASR.”

– Daniel Povey, Associate Research Professor at the Center for Language and Speech Processing at Johns Hopkins University

Which are Some Challenges the Field Currently Faces?

“Speech recognition and the understanding of language is core to the future of search and information, but there are lots of hard problems such as understanding how a reference works, understanding what ‘he’, ‘she’ or ‘it’ refers to in a sentence. It’s not at all a trivial problem to solve in language and that’s just one of the millions of problems to solve in language.”

– Ben Gomes, Head of Search at Google

“With the rise of speech as a primary interface element, one of Mozilla’s main challenges is putting speech-to-text and text-to-speech into Firefox, voice assistants, and opening up these technologies up for broader innovation. Speech has gone from being a “nice-to-have” browser feature to being “table stakes.” It is all but required.”

– Kelly Davis, Machine Learning Researcher at Mozilla

“The ability to recognize speech as well as humans do is a continuing challenge, since human speech, especially during spontaneous conversation, is extremely complex. It’s also difficult to define human performance since humans also vary in their ability to understand the speech of others. When we compare automatic recognition to human performance it’s extremely important to take both these things into account: the performance of the recognizer and the way human performance on the same speech is estimated.”

– Julia Hirschberg, Professor and Chair at the Department of Computer Science at Columbia University.

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