We don’t often think of machines and technology as having the ability to think, learn or adapt. In the past, these abilities were considered exclusive to human beings. However, artificial intelligence is changing our understanding of machines and their capabilities. Recent advancements are showcasing the seemingly limitless potential of modern technology.
Machine learning streamlines many tasks that previously required manual processes or tedious technological workflows. One example of an industry that benefits significantly from recent innovations in artificial intelligence is transcription. Transcription technology is valuable for making audio and video content more accessible and impactful. Let’s discuss the role of machine learning and AI in transcription technology and explore some of the pros and cons of using AI-powered digital transcription solutions to support modern consumers.
Neural Networks and Machine Learning: The Basics
Machine learning is a subset of artificial intelligence that develops algorithms and statistical models that enable computers to perform specific tasks even beyond those that engineers programmed them to complete. Computer systems can identify patterns and make predictions based on input through constant data analysis and repetition. Over time, computers learn from these repetitions to become more efficient, accurate and reliable.
One common framework for machine learning systems is a neural network. A neural network is a computational model similar to the human brain. It mimics how a human brain processes information and makes connections to act and adapt. Neural networks can serve various tasks, including classification, regression, pattern recognition, image and speech recognition and speech data processing, making them well-suited for tasks like transcription and speech-to-text conversion.
Automatic Speech Recognition
Machine learning and neural networks play a critical role in a process known as automatic speech recognition or ASR. ASR technology converts audio input to written text using advanced language models developed by machine learning. ASR powers many popular speech recognition systems, including virtual assistants, dictation tools and transcription software.
ASR systems typically consist of several different components, such as:
- Acoustic models
- Language models
- Lexicons
- Decoders
Each component synthesizes and interprets audio information to accurately represent it as text. ASR systems will break audio samples into bite-sized or “phonemes” and use their machine learning models and neural networks to determine how best to represent those sounds as words, phrases and sentences. This type of voice-to-text conversion works quickly with the right software solution, which makes it capable of supporting complex processes like real-time transcription and live captioning.
Deep Learning and Transcription
In the past, transcription required a manual process in which a single individual would listen to an audio recording and write down or type out everything they heard. Thanks to advancements in artificial intelligence technology, the transcription process requires minimal human intervention.
Automatic transcription solutions primarily rely on advanced machine learning, known as deep learning, to interpret complex data sets like audio recordings. Deep learning uses multi-layered neural networks to create the most accurate textual representations of audio input possible. Deep learning algorithms become more adept at understanding complex data sets over time to complete the speech pattern analysis process more quickly and efficiently. Deep learning models can enhance transcription tools’ accent recognition capabilities and improve overall transcription accuracy.
Natural Language Processing
Across the board, deep learning techniques are changing how computers interpret and respond to human speech. For example, deep learning has played a critical role in the advancement of Natural Language Processing or NLP. NLP is a type of artificial intelligence that focuses on computer-human language interactions and uses deep learning models to help computers strengthen their understanding of human speech.
Through AI-powered NLP, computers can more fully grasp the nuances of human speech and communication to generate written text that is both meaningful and useful to the average consumer. Regarding the role of NLP in transcription, this type of artificial intelligence is critical for improving the coherence of transcripts. The deep learning techniques in NLP enable computers to interpret language subtleties more accurately and enhance their contextual understanding of spoken text.
Artificial Intelligence (AI) and Transcription
Artificial intelligence makes digital transcription more accessible, affordable and efficient for the average consumer. AI-powered transcription tools can support various media projects and virtual communications. Many popular media hosting and communication platforms feature built-in automatic transcription features that rely upon machine learning techniques to convert speech to text in real-time.
While AI learning models lay a solid framework for digital transcription and automatic speech recognition, it is essential to remember that these technologies are still evolving. Speech recognition algorithms make speech-to-text technology extremely commonplace, but ongoing advancements are necessary to ensure these tools achieve the highest possible accuracy rates.
Even with their advanced data handling practices and voice recognition capabilities, computers’ limited understanding of the nuances of human speech may impede their ability to perform accurate word conversions consistently. However, deep learning models will enable computers to learn as they go for greater speech pattern adaptation over time. This ongoing process should contribute to a more comprehensive understanding of language for digital transcription systems performing accessibility-related purposes.
Transcription Services and AI: Use Cases and Considerations
AI transcription services offer clients unparalleled efficiency and cost-effectiveness when it comes to producing transcripts of their audio and video content. However, it is important to understand the limitations of automated transcription in delivering accuracy rates capable of supporting today’s accessibility guidelines.
While computers and their deep learning models led to the development of many scalable transcription solutions, the accuracy rates of these cost-effective transcription tools often have shortcomings when handling complications, including:
- Poor audio quality
- Multiple speakers
- Cross-talk
- Background noise
- Diverse dialects and accents
Developers hope that NLP software will continue to enhance its accuracy rates as learning models become more capable of controlling for these variables. In the interim, however, it is important to take additional quality control steps to ensure that all AI-generated transcripts achieve the level of transcription accuracy dictated by accessibility standards like the ADA and WCAG.
Transcription software can help businesses across nearly every industry streamline their record-keeping processes and improve the quality of their communications. Transcription is also a critical tool within the media industry because transcripts can help creators make their audio and video content more accessible to audience members with disabilities.
However, depending on your industry and use case, it might also be necessary to consider whether AI-powered transcription solutions sufficiently prioritize the safety and security of your sensitive data. For example, solutions for legal transcription and transcription in healthcare must employ high-level data security protocols to protect the confidentiality of clients and industry professionals. Transcription projects for the financial sector must also be completed with strict adherence to data security and confidentiality regulations.
What is the Best Alternative to AI Transcription?
If you’re looking for a cost-effective alternative to AI transcription that delivers top-tier accuracy rates while prioritizing data security, consider hybrid transcription models like Verbit’s. Verbit uses a dual approach to transcription that combines highly advanced AI model training with the expertise and experience of human transcribers to produce highly accurate final transcripts that professionals across every industry can use.
In addition to balancing efficiency and accuracy, Verbit’s transcription platform is cost-effective, versatile and reliable. Verbit’s approach delivers more comprehensive final transcripts than machine learning applications by incorporating human oversight. Verbit also offers real-time captioning and transcription capabilities, so users can reliably support live communications like conference calls, webinars, livestreams and more. Verbit’s platform uses advanced data security protocols to protect users and their information throughout every step of the transcription process.
Partnering with Verbit for Tech that Improves Accessibility
Machine learning has revolutionized our understanding of technology and its limits. Recent machine learning advancements in data science and computer programming allow us to find new ways of going about our day-to-day tasks and contribute to skyrocketing levels of efficiency and productivity in the workplace and beyond. Tools like AI-powered transcription have made it faster and easier than ever to enhance the quality of both content and communications while improving accessibility for individuals with disabilities.
Verbit is proud to offer a wide variety of sophisticated transcription solutions that combine all the best parts of artificial intelligence software with the tried-and-true techniques of professional human transcribers. With a platform that is accurate, affordable and readily accessible, Verbit gives creators and professionals across every industry top-quality communication technology solutions like captioning and transcription. To learn more about machine learning, artificial intelligence or the future of transcription technology, reach out today to speak to a member of the Verbit team.