Artificial Intelligence (AI) is the science behind the automation of many of our daily activities. The purpose is to eliminate error and provide us humans with more time to channel our skills towards more complex, value-generating tasks. By teaching machines to think and act like us, we can significantly reduce human effort from both small and large scale processes. However, in order to do this successfully, we need to train computer systems to learn specific operations so they can carry them out themselves.
At present, the widespread use of the technology is largely limited to weak AI. This category refers to machines processing specific operations like virtual assistants and other speech or voice recognition related tasks. On the other hand, strong AI focuses on machines being able to handle unspecified functions without any need for human intervention.
Unlike its name, weak AI currently has plenty more uses cases, making it functionally stronger than its more sophisticated counterpart. With increased use and popularity, weak AI technology has more real-life data from which to learn and continually improve. It is for this exact reason that AI model training is key to building precise speech recognition engines.
How Machine Learning Models Contribute to Speech Recognition
Speech recognition technology was one of the earliest applications of Al that showed machines could be programmed to understand us as humans. Great optimism surfaced in 1952 with the first live demo of Audrey, a machine built by Bell Labs in the US, which was able to recognize numbers in speech with over 90% accuracy.
While impressive, this higher rate of accuracy was limited to when the inventor was speaking. With other speakers, Audrey’s level of accuracy was knocked down to 70-80% or less. This raised the simple question: Had Audrey been trained with more speakers would there have been no difference in recognition accuracy? It was this discrepancy that carved the way for model learning to contribute to the field of speech recognition software as a way of problem-solving.
Fast forward to nearly three decades later and, although there had been improved attempts from other companies, it was Apple’s launch of virtual assistant Siri in 2011 that put speech recognition on the map for the masses. iPhone users were thrilled at the prospect of their mobile phones being able to respond to voice commands using speech to text technology.
It wasn’t long before the speech recognition software behind Siri began to disappoint with low accuracy and a poor ability to drown out any background noise. Other big companies, such as Google and Microsoft, also released their own virtual assistants around the same time. Despite this, their collective software shortcomings kept users engaged with some pretty amusing incorrect results for users.
User eagerness for the technology meant all these companies had access to abundant speech data. These large volumes of real-life data were perfect for feeding into different training models to build up more robust machine learning algorithms. This is the key to improving performance and broadening the functionality of speech recognition technology.
Why Transcription Is At the Heart of Building Speech Recognition Engines
At TranscribeMe, we deal with vast amounts of speech data for conversion into digital transcripts on a daily basis. One of the main factors that affect transcription accuracy is recording quality. Human transcriptionists are far more likely to understand a voice recording with background noise issues or multiple speakers than a machine. By offering both services, we can easily identify high-quality speech data from low-quality recordings.
To ensure the creation of a successful speech recognition engine, using enough high-quality data to train machine learning algorithms is paramount. Even where an algorithm has been built correctly, its performance still very much depends on the accuracy of the datasets it was trained with. In always striving for accuracy with our services while serving a number of different industries, we are able to continuously train our automatic speech recognition technology with incredibly precise data sets.
Speech recognition is increasingly becoming of interest for businesses due to two main reasons: the rising number of voice command users and the valuable information speech data holds. Analysis of speech recognition data offers great business intelligence to help better shape company strategy.
We have the ability to produce the highest quality human-annotated and verified corpora training sets for machine learning and speech recognition training. These capabilities can be applied across languages, accents, and other data points in line with your enterprise needs. Ready to experience transcription like never before? Contact our sales team to request a demo and boost your business today!
