Massive Development in Voice and Speech Recognition Market: Researchers Create a Simple Technique that can help Speech Recognition Systems to be Widely Deployed

Posted On November 11, 2021     

Automated speech-recognition technology is gaining popularity as people get accustomed to virtual assistants like Siri. However, the effectiveness of these systems is confined to the world's most frequently spoken language, which has a population of only 7,000 people. As these technologies cannot cater to uncommon languages, millions of people who speak them are deprived of conveniently using those systems that rely on speech, such as translation services, smart home devices, and assistive technologies. Constant developments in the field have facilitated the evolution of the machine learning model, and learning the world's uncommon language is no longer a mere theory. Now, even those languages that do not have a large amount of transcribed speech can be used to train algorithms. However, these solutions are highly sophisticated and expensive, making them hard to be widely applicable.
 
A research team may have managed to tackle the problem as they unveiled a simplistic method to decrease the complexity surrounding the advanced speech-learning model. The technique would ensure that these models can run efficiently and accomplish better performance than present methods. The development would also boost the Voice and Speech Recognition Market. The method would help level the playing field while also bringing the speech-recognition system to a wide range of areas where they have to be deployed.
 
The team narrated that their method revolves around removing the redundant part of the standard but sophisticated recognition model. Then minor adjustments are made to provide it with the ability to recognize a particular language. Researchers revealed that only minor changes are required after the larger model is shrunk down to size. Teaching this model a foreign language is substantially less expensive and time-consuming.
 
The system has a large number of applications. It can be used in various academic environments for helping children who are partially or entirely blind. Further, it can also be advantageous in health care settings by using medical transcription, thus increasing efficiency. In addition, the technique can also be applied within the legal field for court reporting.
 
Automatic speech recognition can also be beneficial for users learning a new language as they can enhance their pronunciation skills. The system could even be an addition as a transcribe to help rare document languages that are at risk of becoming extinct.
 
The researchers are set to develop their model further and undertake tests to see how it can benefit other deep learning networks.

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