As technology keeps progressing, most aspects of our lives have felt its effects, and most of it has been for the better. There are very few areas of our lives that science has not had an impact on, and one of the biggest revolutions in science right now is in the field of Artificial Intelligence and automation. A direct segue of the AI field is none other than voice-recognition. Text-to-speech devices have evolved drastically and have gotten better over the years. Here’s a look at how far they have come and why they are so important.
The Present Problem
The most significant hurdle Machine Learning and AI enthusiasts have is that software that runs on such technology requires quite a bit of training to get accurate. The period of training varies from one algorithm to another, but the underlying concept behind this technique is that the device needs to get accustomed to a person’s voice and hence needs multiple iterations to become accurate enough to use efficiently. To make them sound natural in conversation, might take months of determined practice and training, which becomes a hindrance as far as marketing the product is concerned.
Speech recognition programs utilise algorithms that work on acoustic and linguistic modelling which use the link between speech and audio signals to match the sounds that it receives with word sequences by listening to it and studying the audio signal it produces. Hence as it requires multiple levels of integration and study, the process takes some time. But now, as technology has progressed further, scientists believe that they have found a solution to this problem. Recently, Microsoft, along with a group of Chinese researchers, announced that they had perfected a text-to-speech device that can replicate a human voice with just 20 minutes of training. The method requires only 200 voice samples to generate a realistic sound and manner of speech.
Mimicking the Human Brain
This AI works on the principle of Transformers, wherein a particular network which resembles the nervous system used by humans to function efficiently is used to replicate the sound we create. This sort of neural networking architecture has been gaining momentum for a while now due to its high rate of productivity and closeness to how we function as individuals as it mimics how our synapses communicate with each other to pass information and gather inputs from the various sensory organs. Such a well-connected system is able to process longer sequences and more complex words and sentences better, leading to faster learning curves that allow the device to pick up new words and voices in a more efficient fashion.
Why It’s Better
Such a system hence works well for sentences that have an array of words with different phonetics and methods on enunciation. The AI-powered engine can, therefore, decipher which words or syllables to stress on and which to avoid pronouncing all together. The primary issue that older devices had was that they sounded entirely mechanical due to their lack of proper diction. This is the exact issue that neural networks are now efficiently weeding out by studying the speech as sentences at a time rather than paying attention to each word.
While the results are not perfect yet as sometimes when it comes to complex sentences, the device still sounds a little robotic, but the end result is much more human sounding and accurate. But this AI is still a vast improvement over all other present systems and has an accuracy rating of above 99.84 per cent. But the most significant advantage they provide is that they take very less time to train hence making the entire recognition curve a lot faster and cheaper. It would also make such voice-recognition systems more accessible for people who genuinely need them by simplifying the logistics involved.
Communication has undergone a significant upheaval due to advancements in science and technology. For example, most large conglomerates now use AI-assisted voice assistants to manage their customer care calls and service advertisement calls. We rarely ever hear a human answer our calls because the truth is that on a large scale, machines are cheaper and more efficient. We have almost gotten used to the automatic voice recording that guides us by asking us to press buttons so as to navigate through an in-built catalogue.
The biggest hurdle that such technology has from becoming dominant is that it can be unreliable at times due to issues in detecting variable accents and dialects. Sometimes, due to variation in emphasis or due to some other genetic speech impediments, certain words aren’t comprehensible to the platform, and this can lead to erroneous results. But as technology evolves, we see a rapid improvement in the accuracy of such systems, and very soon we might come across a design that will accurately convert text to speech with minimal training and calibration.