With the introduction of voice recognition assistants like Siri, Google Assistant, Cortana, Alexa, and others, speech recognition technology has started to replace the way humans interact with digital devices.
The State of Speech Recognition
In the recent past, there have been many breakthroughs in speech recognition technology. The technological breakthroughs haven’t just greatly matured the speech recognition technology, but it also has made it a viable technology ready to replace the traditional ways humans interact with digital devices. The rapid uptake of speech recognition technology and the availability of various speech to text software has only made life easier for humans. The technology has made way for more efficient workflow processes, as well as opened up possibilities that were once only considered as “miraculous”.
Today, there is wide-ranging applicability of speech to text software across industries, which continues to grow. From the healthcare industry to research, and from customer service to marketing and journalism the speech-to-text software is just making it easier for humans to communicate with digital devices.
What’s the need for automated transcription?
Various professions need precise and rapid transcripts to perform daily routine tasks effectively. The technology behind speech to text software makes it easy for professionals to get their hands on accurate, reliable, and affordable transcription, as compared to manual transcriptions.
And while the technology may still lag (just a little) the human performance, there are various speech to text software offering accuracy of 95% and above. Not to forget, with this high-level accuracy, they also bring in the convenience of automation that ensures instant and affordable access transcriptions without any human involvement.
Opening Paths to Digital Accessibility
Well, automated transcription isn’t the sole reason for attraction for speech to text software, rather the technology has much more to offer. One of the primary reasons for the rapid adaptation of technology across the work is the more possibilities of digital accessibility brought by the technology. In view of Europe, digital accessibility isn’t just a choice or privilege but a compulsion. As per the EU Directive 2016/2012, Governments are responsible to ensure equal access to information for all citizens. In this case, the information also includes audio recordings, podcasts, or videos. This means that speech-to-text software is being used to generate auto-captions or transcripts for digital information that could be accessible to all people including people with hearing disabilities.
The Technology behind Speech Recognition!
An automated speech recognition system is at the core of speech to text software. To put it simply, a speech recognition system comprises acoustic and linguistic components, which run on single or multiple computers.
Among the two components, the acoustic component of the technology is responsible for converting audio into digital signals (yup the same analog vibrations created by sound which you must have seen in your science book). Nonetheless, once the audio is broken into digital signals, it is then matched with the “Phonemes” (ok, we know it’s getting a little too technical). Phonemes represent the sounds generated to form meaningful expressions in our language. The role of the acoustic component ends here.
Next, the linguistic components come into play and convert these small acoustic units into meaning words, and phrases. The real miracle of speech recognition technology is to differentiate between words that sound almost similar but have entirely different meanings like “Their” and “There”.
The differentiation between words is done by the linguistic component, which analysis the preceding words to understand the context of the sentence and make the right choice. This is achieved by the speech to text software using “Hidden Markov Models”, which are widely used across different speech recognition software.
Now, to offer users accurate transcription, the acoustic and linguistic components must be trained rigorously for a specific language. And it’s the training of these acoustic and linguistic components that eventually determine the accuracy level offered by any speech to text software.
Wait… There’s one more model!
Well, while the acoustic and linguistic components make up the core of the technology, there’s another model that we are sure you must have used – the Speaker Model.
Yes, the speaker-dependent models represent the widely used virtual assistant. However, these models have to be trained for specific voices. For instance, you can train your home virtual assistant (Siri, Google Speech to text api, Cortana, or Alexa) to only recognize your voice (essentially making the model depend on the speaker).
Speaker dependent models generally offer even higher accuracy for case-specific use. However, it does require additional training time. Also, speaker-dependent models aren’t flexible to be used in different settings; like in conferences or group meetings.
Are All Speech Recognition Tools the Same?
There are various speech-to-text software available in the market, each with its own strengths, limitations, and use cases. For instance, while some speech to text softwares are great to perform repetitive tasks, others may offer better flexibility to perform under different settings.
Speech to Text Software. Expectations vs. Reality
While speech recognition technology has come a long way in the last decade, it still faces various challenges. Some of the current limitations which are yet to overcome by technology includes;
– Recording conditions:
Similar to human transcriptions, the quality and accuracy of automated transcriptions by speech to text software is heavily influenced by recording conditions. The software may find it hard to interpret the words from the audio with a noisy background environment or audio with multiple speakers speaking simultaneously.
– Recognizing Accents and Dialects
Another key limitation of speech to text software is the ability to adhere to different dialects and accents. Language inherently has a complex structure, while every person also has his/her speaking style. The additional dialects and accents make it even harder for the speech recognition models to understand words correctly. However, this limitation may be greatly overcome by training the model with different data types.