Editing Large Vocabulary Continuous Speech Recognition (section)

== Impact ==
Over the past decade or so, several advances have been made to the design of modern LVCSR systems to the point where their application has broadened from early speaker-dependent dictation systems to speaker-independent automatic broadcast news transcription and indexing, lectures and meetings transcription, conversational telephone speech transcription, open-domain voice search, medical and legal speech recognition, and call center applications, to name a few. The commercial success of these systems is an impressive testimony to how far research in LVCSR has come.<ref>Saon, G., & Chien, J.-T. (2012). ''Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances. IEEE Signal Processing Magazine, 29(6), 18–33.'' doi:10.1109/msp.2012.2197156</ref> LVCSR technology has significantly advanced the capabilities of speech recognition, enabling a wide range of applications and benefiting numerous sectors.

=== Improved Accuracy and Enhanced User Experience ===

* LVCSR has substantially improved the accuracy of speech recognition systems. It allows for the transcription of continuous speech and recognition of a vast vocabulary, resulting in more precise and natural language understanding. On the other side, LVCSR has made voice interactions more user-friendly and intuitive. Users can communicate naturally and expect more accurate responses from speech-based systems.
* The higher accuracy of LVCSR has made it more practical and reliable for various applications, such as voice assistants like [[wikipedia:Siri|Siri]], [[wikipedia:Google_Assistant|Google Assistant]], and [[wikipedia:Amazon_Alex|Alexa]], transcription services like [https://riverside.fm/transcription Riverside], [https://otter.ai/ Otter].

=== Automation and Accessibility ===

* LVCSR technology enables automation in various sectors, reducing the need for manual intervention and streamlining processes, and improving accessibility for individuals with disabilities, especially those with hearing impairments or speech disabilities.
* LVCSR has found applications like [https://www.deepscribe.ai/ Deepscribe], [https://www.nuance.com/healthcare.html Nuance DAX] in medical informatics, where healthcare professionals work alongside speech recognition experts to develop systems for transcribing medical records, aiding in diagnosis, and facilitating medical research. [https://whispp.com/ Whispp], smart speech amplifier enables people with a voice disorder to communicate smoothly and effectively, opening up new opportunities for them.

=== Research Opportunities and Diverse Applications ===

* LVCSR technology has spurred research in [[wikipedia:Natural_language_processing|NLP]], AI, and related fields, offering opportunities for innovation and development. LVCSR has also catalyzed extensive research in emerging areas and fostered interdisciplinary collaborations, generating numerous innovative research topics
* Researchers have explored the intersection of neuroscience and LVCSR to better understand how the human brain processes spoken language. Linguists and acoustic modeling experts collaborate to improve speech recognition systems by enhancing phonetic models and language-specific features, making them more effective in recognizing dialects and accents. The intersection of NLP and LVCSR has led to significant progress in understanding and processing spoken language. Researchers in these fields work together to develop advanced algorithms for LVCSR and conversational AI.