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!
