A good synthesis of the current landscape of speech recognition and how artificial intelligence can improve and expand this technology in the future.
Includes
- explanation of technology
- market growth calculations
- current uses and future possibilities
“There are so many benefits that these types of technologies provide to advance our world forward from: increasing productivity in many businesses, such as in healthcare to detect depression, to analyze mood(s), reduce overhead in typing up client sales notes as automatic transcription allows immediate filings from zoom calls, etc, helping those with speech or sight challenges.”
https://www.forbes.com/sites/cindygordon/2021/12/23/a-market-to-harness-speech-recognition-artificial-intelligence-ai-innovations-on-the-rise/?sh=4f872084134d
Being written in 2021, I wonder to what extent the projections in this article have been satisfied, surpassed or fallen short. Regardless of this, I do think that the speech recognition market is growing and is being advanced rapidly through its direction connection to AI technologies. In the classroom, I also see the importance of speech recognition in assisting with communication. For example, the use of captions are increasingly available as an assistive technology in the classroom through a range of apps. Google Slides now allows presenters to have live captions generated onto their presentation. I have also seen a large uptake in students utilizing speech to text functions like “voice dictation” on iPads. Nonetheless, while speech recognition technologies are improving, there are still minor errors in the technologies that point to room for growth. For instance, I have seen Youtube videos unintentionally present inappropriate captions while watching a video with my students simply due to a gap in the technologies ability to discern certain words. I also struggle with speech recognition with Google Home sometimes as it unable to distinguish certain commands.
Speech recognition and AI technologies at the present time presents a fascinating and challenging concept. AI used in this way assumes that all linguistic challenges have been met, and that the ability to create a well sculpted text for a valid response exists. Captions are useful in the short term but does this indicate a likelihood of receiving a response under a similar banner ? and if so, what are the outcomes?
Hi Lowell, thank you for highlighting speech recognition and artificial intelligence. In recent year, these two technologies have made significant advancements.
In terms of speech recognition, mega-companies like Amazon, Google, Microsoft and YouTube have developed powerful speech recognition systems that can understand and transcribe speech effectively. It’s not perfect as for transcribing accents, slangs, and dialects. But it serves a good purpose for the common good.
Then when it comes to artificial intelligence, it is being increasingly integrated into educational practices. Now more than ever, as chatbots and virtual assistants come into the play. I wonder what does the future look like for education?
With voice recognition, it would be fun and enriching to see some multi-model interaction, such as gesture recognition, facial expression analysis, and gaze tracking, that will enhance social and emotional facets. As for AI, I have more concerns on its ethical practices rather than its capacity to doing thing. I’m sure it’s going to be smarter and quicker. I worry it may backfire to us at some point, if we don’t regulate it well enough.
In my opinion, the prediction, or the trend of voice recognition market’s growth is highly valuable for educators. It suggests a potential in developing more educational tools that leverage voice recognition and AI to create more interactive learning, better teaching methods and improved accessibility. It could provide more personalized learning and accommodation for students with different learning styles. In addition, learning specialists can benefit from predicting future trend, and focus on creating better learning solutions based on the demand of speech recognition softwares. The trends can also guide entrepreneurs in identifying potential areas for innovation and investment. Overall, this is one of the areas I am very interested in, and I think it has great potential to improve educational experiences in the future.
Really interesting to revisit this Market Projection. Nick Robitaille’s point back in 2023 about whether these projections have been met is a good one. Now that it’s 2026, I would say that by most accounts this is one of the edtech markets that has grown faster than expected. Since the release of the Forbes 2021 article referenced by Lowell, latency and response times of AI powered speech recognition have improved dramatically, and it is interesting to consider what those improvements will mean for edtech ventures and the application of this tech in educational contexts going forward.
Specifically speaking to the advancements of AI and speech recognition tech, I believe we are getting to a point where this technology is becoming fast enough that feedback can happen in the middle of a conversation or thought, rather than requiring a pause or moment to generate feedback. A technological shift in the “snappiness” of these tools could fundamentally change the nature and value of the feedback we experience through using it and the impact it has towards the quality of the learning that takes place overall. A voice AI that listens, adapts, and corrects in real time might get us closer to one on one teacher-instruction at a greater scale which might become something group teaching contexts have never been able to offer individual learners.
Beyond that, advancements in AI enhanced speech recognition have the potential to better serve students who think more fluently in speech than in text, or those navigating between languages, extending learning experiences beyond the physical act of typing.
What I am fascinated in is considering how these technologies will affect learners of tonal languages, as well as what the future holds for its application in music education contexts where a natural extension of speech recognition tech could be towards pitch recognition, ear training and aural skills, vocal performance, vocal expression, and choral diction. Could AI assisted speech recognition eventually turn towards recognizing notes, timbre, and harmony on a musical instrument, or be used to auto-transcribe an improvised solo into music notation?