TY  - JOUR
A1  - Ward, Nigel G.
A1  - Vega, Alejandro
A1  - Baumann, Timo
T1  - Prosodic and temporal features for language modeling for dialog
JF  - Speech communication
N2  - If we can model the cognitive and communicative processes underlying speech, we should be able to better predict what a speaker will do. With this idea as inspiration, we examine a number of prosodic and timing features as potential sources of information on what words the speaker is likely to say next. In spontaneous dialog we find that word probabilities do vary with such features. Using perplexity as the metric, the most informative of these included recent speaking rate, volume, and pitch, and time until end of utterance. Using simple combinations of such features to augment trigram language models gave up to a 8.4% perplexity benefit on the Switchboard corpus, and up to a 1.0% relative reduction in word error rate (0.3% absolute) on the Verbmobil II corpus.
KW  - Dialog dynamics
KW  - Dialog state
KW  - Prosody
KW  - Interlocutor behavior
KW  - Word probabilities
KW  - Prediction
KW  - Perplexity
KW  - Speech recognition
KW  - Switchboard corpus
KW  - Verbmobil corpus
Y1  - 2012
U6  - https://doi.org/10.1016/j.specom.2011.07.009
SN  - 0167-6393
VL  - 54
IS  - 2
SP  - 161
EP  - 174
PB  - Elsevier
CY  - Amsterdam
ER  -