Learn about speech recognition technology—how speech to text software works, benefits, limitations, transcriptions, and other real world applications.
Whether you’re a professional in need of more efficient transcription solutions or simply want your voice-enabled device to work smarter for you, this guide to speech recognition technology is here with all the answers.
Few technologies have evolved rapidly in recent years as speech recognition. In just the last decade, speech recognition has become something we rely on daily. From voice texting to Amazon Alexa understanding natural language queries, it’s hard to imagine life without speech recognition software.
But before deep learning was ever a word people knew, mid-century were engineers paving the path for today’s rapidly advancing world of automatic speech recognition. So let’s take a look at how speech recognition technologies evolved and speech-to-text became king.
What Is Speech Recognition Technology?
With machine intelligence and deep learning advances, speech recognition technology has become increasingly popular. Simply put, speech recognition technology (otherwise known as speech-to-text or automatic speech recognition) is software that can convert the sound waves of spoken human language into readable text. These programs match sounds to word sequences through a series of steps that include:
- Pre-processing: may consist of efforts to improve the audio of speech input by reducing and filtering the noise to reduce the error rate
- Feature extraction: this is the part where sound waves and acoustic signals are transformed into digital signals for processing using specialized speech technologies.
- Classification: extracted features are used to find spoken text; machine learning features can refine this process.
- Language modeling: considers important semantic and grammatical rules of a language while creating text.
How Does Speech Recognition Technology Work?
Speech recognition technology combines complex algorithms and language models to produce word output humans can understand. Features such as frequency, pitch, and loudness can then be used to recognize spoken words and phrases.
Here are some of the most common models for speech recognition, which include acoustic models and language models. Sometimes, several of these are interconnected and work together to create higher-quality speech recognition software and applications.
Natural Language Processing (NLP)
“Hey, Siri, how does speech-to-text work?”
Try it—you’ll likely hear your digital assistant read a sentence or two from a relevant article she finds online, all thanks to the magic of natural language processing.
Natural language processing is the artificial intelligence that gives machines like Siri the ability to understand and answer human questions. These AI systems enable devices to understand what humans are saying, including everything from intent to parts of speech.
But NLP is used by more than just digital assistants like Siri or Alexa—it’s how your inbox knows which spam messages to filter, how search engines know which websites to offer in response to a query, and how your phone knows which words to autocomplete.
Neural networks are one of the most powerful AI applications in speech recognition. They’re used to recognize patterns and process large amounts of data quickly.
For example, neural networks can learn from past input to better understand what words or phrases you might use in a conversation. It uses those patterns to more accurately detect the words you’re saying.
Leveraging cutting-edge deep learning algorithms, neural networks are revolutionizing how machines recognize speech commands. By imitating neurons in our brains and creating intricate webs of electrochemical connections between them, these robust architectures can process data with unparalleled accuracy for various applications such as automatic speech recognition.
Hidden Markov Models (HMM)
The Hidden Markov Model is a powerful tool for acoustic modeling, providing strong analytical capabilities to accurately detect natural speech. Its application in the field of Natural Language Processing has allowed researchers to efficiently train machines on word generation tasks, acoustics, and syntax to create unified probabilistic models.
Speaker diarization is an innovative process that segments audio streams into distinguishable speakers, allowing the automatic speech recognition transcript to organize each speaker’s contributions separately. Using unique sound qualities and word patterns, this technique pinpoints conversations accurately so every voice can be heard.
The History of Speech Recognition Technology
It’s hard to believe that just a few short decades ago, the idea of having a computer respond to speech felt like something straight out of science fiction. Yet, Fast-forward to today, and voice-recognition technology has gone from being an obscure concept to becoming so commonplace you can find it in our smartphones.
But where did this all start? First, let’s take a look at the history of speech recognition technology – from its uncertain early days through its evolution into today’s easy-to-use technology.
Speech recognition technology has existed since the 1950s when Bell Laboratory researchers first developed systems to recognize simple commands. However, early speech recognition systems were limited in their capabilities and could not identify more complex phrases or sentences.
In the 1980s, advances in computing power enabled the development of better speech recognition systems that could understand entire sentences. Today, speech recognition technology has become much more advanced, with some systems able to recognize multiple languages and dialects with high accuracy.
Timeline of Speech Recognition Programs
- 1952 – Bell Labs researchers created “Audrey,” an innovative system for recognizing individual digits. Early speech recognition systems were limited in their capabilities and could not identify more complex phrases or sentences.
- 1962 – IBM shook the tech sphere in 1962 at The World’s Fair, showcasing a remarkable 16-word speech recognition capability – nicknamed “Shoebox”—that left onlookers awestruck.
- 1980s – IBM revolutionized the typewriting industry in the 1980s with Tangora, a voice-activated system that could understand up to 20,000 words. Advances in computing power enabled the development of better speech recognition systems that could understand entire sentences.
- 1996 – IBM’s VoiceType Simply Speaking application recognized 42,000 English and Spanish words.
- 2007 – Google launched GOOG-411 as a telephone directory service, an endeavor that provided immense amounts of data for improving speech recognition systems over time. Now, this technology is available across 30 languages through Google Voice Search.
- 2017 – Microsoft made history when its research team achieved the remarkable goal of transcribing phone conversations utilizing various deep-learning models.
How is Speech Recognition Used Today?
Speech recognition technology has come a long way since its inception at Bell Laboratories.
Today, speech recognition technology has become much more advanced, with some systems able to recognize multiple languages and dialects with high accuracy and low error rates.
Speech recognition technology is used in a wide range of applications in our daily lives, including:
- Voice Texting: Voice texting is a popular feature on many smartphones that allow users to compose text messages without typing.
- Smart Home Automation: Smart home systems use voice commands technology to control lights, thermostats, and other household appliances with simple commands.
- Voice Search: Voice search is one of the most popular applications of speech recognition, as it allows users to quickly
- Transcription: Speech recognition technology can transcribe spoken words into text fast.
- Military and Civilian Vehicle Systems: Speech recognition technology can be used to control unmanned aerial vehicles, military drones, and other autonomous vehicles.
- Medical Documentation: Speech recognition technology is used to quickly and accurately transcribe medical notes, making it easier for doctors to document patient visits.
Key Features of Advanced Speech Recognition Programs
If you’re looking for speech recognition technology with exceptional accuracy that can do more than transcribe phonetic sounds, be sure it includes these features.
Advanced speech recognition programs use acoustic training models to detect natural language patterns and better understand the speaker’s intent. In addition, acoustic training can teach AI systems to tune out ambient noise, such as the background noise of other voices.
Speaker labeling is a feature that allows speech recognition systems to differentiate between multiple speakers, even if they are speaking in the same language. This technology can help keep track of who said what during meetings and conferences, eliminating the need for manual transcription.
Advanced speech recognition programs allow users to customize their own dictionaries and include specialized terminology to improve accuracy. This can be especially useful for medical professionals who need accurate documentation of patient visits.
If you don’t want your transcript to include any naughty words, then you’ll want to make sure your speech recognition system consists of a filtering feature. Filtering allows users to specify which words should be filtered out of their transcripts, ensuring that they are clean and professional.
Language weighting is a feature used by advanced speech recognition systems to prioritize certain commonly used words over others. For example, this feature can be helpful when there are two similar words, such as “form” and “from,” so the system knows which one is being spoken.
The Benefits of Speech Recognition Technology
Human speech recognition technology has revolutionized how people navigate, purchase, and communicate. Additionally, speech-to-text technology provides a vital bridge to communication for individuals with sight and auditory disabilities. Innovations like screen readers, text-to-speech dictation systems, and audio transcriptions help make the world more accessible to those who need it most.
Limits of Speech Recognition Programs
Despite its advantages, speech recognition technology still needs to be improved.
- Accuracy rate and reliability – the quality of the audio signal and the complexity of the language being spoken can significantly impact the system’s ability to accurately interpret spoken words. For now, speech-to-text technology has a higher average error rate than humans.
- Formatting – Exporting speech recognition results into a readable format, such as Word or Excel, can be difficult and time-consuming—especially if you must adhere to professional formatting standards.
- Ambient noise – Speech recognition systems are still incapable of reliably recognizing speech in noisy environments. If you plan on recording yourself and turning it into a transcript later, make sure the environment is quiet and free from distractions.
- Translation – Human speech and language are difficult to translate word for word, as things like syntax, context, and cultural differences can lead to subtle meanings that are lost in direct speech-to-text translations.
- Security – While speech recognition systems are great for controlling devices, you don’t always have control over how your data is stored and used once recorded.
Using Speech Recognition for Transcriptions
Speech recognition technology is commonly used to transcribe audio recordings into text documents and has become a standard tool in business and law enforcement. There are handy apps like Otter.ai that can help you quickly and accurately transcribe and summarize meetings and speech-to-text features embedded in document processors like Word.
However, you should use speech recognition technology for transcriptions with caution because there are a number of limitations that could lead to costly mistakes.
If you’re creating an important legal document or professional transcription, relying on speech recognition technology or any artificial intelligence to provide accurate results is not recommended. Instead, it’s best to employ a professional transcription service or hire an experienced typist to accurately transcribe audio recordings.
Human typists have an accuracy level of 99% – 100%, can follow dictation instructions, and can format your transcript appropriately depending on your instructions. As a result, there is no need for additional editing once your document is delivered (usually in 3 hours or less), and you can put your document to use immediately.
Unfortunately, speech recognition technology can’t achieve these things yet. You can expect an accuracy of up to 80% and little to no professional formatting. Additionally, your dictation instructions will fall on deaf “ears.” Frustratingly, they’ll just be included in the transcription rather than followed to a T. You’ll wind up spending extra time editing your transcript for readability, accuracy, and professionalism.
So if you’re looking for dependable, accurate, fast transcriptions, consider human transcription services instead.
Is Speech Recognition Technology Accurate?
The accuracy of speech recognition technology depends on several factors, including the quality of the audio signal, the complexity of the language being spoken, and the specific algorithms used by the system.
Some speech recognition software can withstand poor acoustic quality, identify multiple speakers, understand accents, and even learn industry jargon. Others are more rudimentary and may have limited vocabulary or may only be able to work with pristine audio quality.
Speaker identification vs. speech recognition: what’s the difference?
The two are often used interchangeably. However, there is a distinction. Speech recognition technology shouldn’t be confused with speech identification technology, which identifies who is speaking rather than what the speaker has to say.
What type of technology is speech recognition?
Speech recognition is a type of technology that allows computers to understand and interpret spoken words. It is a form of artificial intelligence (AI) that uses algorithms to recognize patterns in audio signals, such as the sound of speech. Speech recognition technology has been around for decades.
Is speech recognition AI technology?
Yes, speech recognition is a form of artificial intelligence (AI) that uses algorithms to recognize patterns in audio signals, such as the sound of speech. Speech recognition technology has been around for decades, but it wasn’t until recently that systems became sophisticated enough to accurately understand and interpret spoken words.
What are examples of speech recognition devices?
Examples of speech recognition devices include virtual assistants such as Amazon Alexa, Google Assistant, and Apple Siri. Additionally, many mobile phones and computers now come with built-in voice recognition software that can be used to control the device or issue commands. Speech recognition technology is also used in various other applications, such as automated customer service systems, medical transcription software, and real-time language translation systems.
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