AUTOMATED SPEECH RECOGNITION (ASR)
Automatic Speech Recognition (ASR) is a technology that is used to recognize speech and produce a written or spoken output. It has been used in numerous applications ranging from medical transcription to call center automation. It has become increasingly popular over the last few years due to advances in natural language processing (NLP) and machine learning (ML) algorithms which have enabled more accurate speech recognition. This article will discuss the history, current applications, and future of ASR technology.
The first attempts at speech recognition date back to the 1950s, when researchers explored the use of linear predictive coding (LPC) to recognize words. This technique was used to create an automated voice-response system for the telephone network. However, the accuracy of these systems was limited and they required large amounts of data to train. In the 1970s, Hidden Markov Models (HMMs) were developed which improved the accuracy of speech recognition systems. HMMs have since been used in many ASR systems, and continue to be a popular approach to speech recognition.
In the early 2000s, the use of NLP and ML algorithms in ASR systems began to increase. NLP algorithms allowed for the recognition of more complex and nuanced words and phrases, while ML algorithms enabled the automated learning of acoustic models. The combination of these technologies has allowed ASR systems to become much more accurate and reliable.
Today, ASR technology is used in many areas including healthcare, automotive, and customer service. In healthcare, ASR systems are used for medical transcription, allowing physicians to quickly and accurately generate patient records. In the automotive industry, ASR systems are used in driver assistance and navigation systems. Finally, in customer service, ASR systems are used to automate call center operations and provide customers with quick and accurate responses to their inquiries.
In the future, ASR technology is expected to become even more accurate and reliable. Advances in ML algorithms such as deep learning have enabled the development of more sophisticated speech recognition models. Additionally, the use of NLP algorithms will allow for the recognition of more complex and nuanced words and phrases.
In conclusion, Automatic Speech Recognition (ASR) technology has made significant strides in the last few years and is now being used in numerous applications. It has enabled the automation of medical transcription, driver assistance, and customer service. In the future, ASR technology is expected to become even more accurate and reliable as advancements in ML and NLP algorithms are made.
References
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Bengio, Y., Courville, A., Vincent, P. (2013). Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828. https://doi.org/10.1109/TPAMI.2013.50
Laroche, F., Dupont, S., Gombert, E. (2017). A survey of automatic speech recognition systems. Signal Processing, 134, 14-29. https://doi.org/10.1016/j.sigpro.2017.03.017
Lam, D. (2019). Speech Recognition: Deep Learning and NLP. Retrived from https://www.youtube.com/watch?v=uUO1ZuG9qN8
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