Editing Large Vocabulary Continuous Speech Recognition (section)

== Historical Context ==
The concept of Automatic Speech Recognition began to take shape in the 1950s and 1960s, with early research focused on constructing recognition systems for isolated word speech using rudimentary techniques.

In the 1970s, a groundbreaking development occurred when [[Hidden Markov Models]] (HMMs) were successfully applied to continuous speech recognition systems. This pivotal moment marked the evolution of speech recognition from basic pattern matching approaches to sophisticated statistical probability models.

The 1980s witnessed the emergence of LVCSR. To achieve continuous speech recognition, innovations like Linear Predictive Coding (LPC) and the incorporation of statistical language modeling significantly enhanced the accuracy and resilience of LVCSR systems.<ref>Sameti, H., Veisi, H., Bahrani, M., Babaali, B., & Hosseinzadeh, K. (2011). A large vocabulary continuous speech recognition system for Persian language. ''EURASIP Journal on Audio, Speech, and Music Processing'', ''2011''(1), 6. <nowiki>https://doi.org/10.1186/1687-4722-2011-426795</nowiki></ref>

Entering the 1990s, LVCSR applications gained momentum in the market, finding increasing utility in transcription services, customer support applications, and supplementary tools. 

In the 21st century, fueled by advancements in deep learning and artificial intelligence, acoustic models continued to evolve. Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs) gradually assumed a dominant role in the LVCSR field, further elevating the performance and precision of speech recognition.<ref>Variani, E., Bagby, T., McDermott, E., & Bacchiani, M. (2017). End-to-End Training of Acoustic Models for Large Vocabulary Continuous Speech Recognition with TensorFlow. ''Interspeech 2017'', 1641–1645. <nowiki>https://doi.org/10.21437/Interspeech.2017-1284</nowiki></ref> This widespread adoption paved the way for applications such as real-time transcription and voice commands. Today, research in the LVCSR domain extends across diverse sectors, including healthcare, automotive, education and entertainment propelling innovation in human-computer interaction and assistive technologies.