Exploiting prosodic breaks in language modeling with random forests
By Yi Su, Frederick Jelinek. 2008
5 citations recorded by the source corpus.
We propose a novel method of exploiting prosodic breaks in language modeling for automatic speech recognition (ASR) based on the random forest language model (RFLM), which is a collection of randomized decision tree language models and can potentially ask any questions about the history in order to predict the future. We demonstrate how questions about prosodic breaks can be easily incorporated into the RFLM and present two language models which treat prosodic breaks as observable and hidden variables, respectively. Meanwhile, we show empirically that a finer grained prosodic break is needed for language modeling. Experimental results showed that given prosodic breaks, we were able to reduce the LM perplexity by a significant margin, suggesting a prosodic N -best rescoring approach for ASR.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Wei-Ning Hsu
- Yossi Adi
- Jade Copet
- Emmanuel Dupoux
- Tu Nguyen
- Evgeny Kharitonov
- Abdel-rahman Mohamed
- Ann Lee
Citations in the corpus
- Text-Free Prosody-Aware Generative Spoken Language Modeling (2021, background): “…classification (Shriberg et al., 1998; Ward and Tsukahara, 2000), syntactic parsing (Tran et al., 2017), speech–language pathology (Cohen et al., 2019), ASR (Ostendorf et al., 2003; Shriberg and Stolcke, 2004), and language modeling (Huang and Renals, 2007; Su and Jelinek, 2008; Ward et al., 2012).”