Praveen Chandar, Yasaman Khazaeni, et al.
INTERACT 2017
The acoustic modeling problem in automatic speech recognition is examined from an information-theoretic point of view. This problem is to design a speech-recognition system which can extract from the speech waveform as much information as possible about the corresponding word sequence. The information extraction process is broken down into two steps: a signal-processing step which converts a speech waveform into a sequence of information-bearing acoustic feature vectors, and a step which models such a sequence. We are primarily concerned with the use of hidden Markov models to model sequences of feature vectors which lie in a continuous space. We explore the trade-off between packing information into such sequences and being able to model them accurately. The difficulty of developing accurate models of continuous-parameter sequences is addressed by investigating a method of parameter estimation which is designed to cope with inaccurate modeling assumptions. © 1987.
Praveen Chandar, Yasaman Khazaeni, et al.
INTERACT 2017
Oznur Alkan, Elizabeth M. Daly, et al.
IUI 2018
Michelle X. Zhou, Jennifer Golbeck, et al.
CHI EA 2014
Daniel Smilkov, Han Zhao, et al.
ISM 2010