Context-dependent point process models for keyword search and detection-based ASR
Chunxi Liu, Aren Jansen, Sanjeev Khudanpur
Abstract
The point process model (PPM) for keyword search (KWS) is a whole-word parametric approach that characterizes each query type by the timing of phonetic events observed during its production. In this paper, we first extend the PPM modeling framework to operate on context-dependent phonetic event patterns instead of monophone patterns considered in the past, which provides significant KWS improvements. Second, we use the context-dependent PPMs to drive a detection-based speech recognition architecture thats runs parallel word detectors covering the whole vocabulary and uses the independent detections to construct lattices that can be used for both KWS indexing and LVCSR decoding. This strategy produces significant improvements over the original PPM KWS framework and provides an encouraging first attempt at PPM-based LVCSR.
BibTeX
@inproceedings{icassp2016_contextdependent,
title = {Context-dependent point process models for keyword search and detection-based ASR},
author = {Chunxi Liu and Aren Jansen and Sanjeev Khudanpur},
booktitle = {ICASSP 2016},
year = {2016}
}