Field speech data pose great challenges to statistical

modeling because the speech signal is often intermixed

with extraneous sounds and other environmental noises

that are either too difficult to compensate dynamically or

too expensive to collect sufficient data for proper offline

training. In this paper, we propose a detection based

method in which the speech recognizer can sharply tune to

only the “meaningful” speech and gracefully ignore the

“unwanted” audio segments. The method is designed to be

integrated with the frame synchronous search for a single

pass processing. In contrast to the conventional keyword

spotting techniques, this integration allows the use of the

language model for better predicting the detection targets

during the search. To study its efficacy, we apply the

framework to a spontaneous speech understanding

application where cohesive phrases congruent to the

domain semantics and application context are used as the

salient feature for selective hearing. Experimental results

on the effectiveness of the system in dealing with out of

domain phrases and other spontaneous speech effects are

encouraging.

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In: Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing

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