Editing Large Vocabulary Continuous Speech Recognition (section)

== Future research ==

=== Emotion recognition and speech generation: ===
Combining emotion recognition technology with speech recognition to better understand and synthesize speech with emotional color. This has potential value in applications such as virtual assistants and automated telephone customer service.<ref>Han, Kun & Yu, Dong & Tashev, Ivan. (2014). Speech Emotion Recognition Using Deep Neural Network and Extreme Learning Machine. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. 10.21437/Interspeech.2014-57. </ref>

=== Language modeling ===
Language modeling techniques are investigated by experts, specifically for conversational speech. For example, discriminative language model training methods and language models that leverage conversational speech patterns. Much of the SRI Language Modeling Toolkit (SRI International is an American nonprofit scientific research institute) was developed as a by-product of LVCSR research, and SRI often provides language modeling support to other sites in the LVCSR community.<ref>Stolcke, A., Zheng, J., Wang, W., & Abrash, V. (2011). SRILM at sixteen: Update and outlook. In Proceedings of IEEE automatic speech recognition and understanding workshop (Vol. 5). ASRU: Waikoloa.</ref>

=== Continuous adaptive and incremental learning: ===
Develop LVCSR systems with continuous adaptive capabilities that can adapt to changing environments and user needs, as well as enable incremental learning to continuously improve performance while continuously accumulating data.<ref>J.E. Hamaker, "MLLR: A Speaker Adaptation Technique For LVCSR," ISIP course lecture, Mississippi State University, 1999.</ref>