5 accepted papers
Individual ship detection from underwater audio is the task of deciding whether a specific ship is present, using sound captured by an underwater hydrophone. It is a task analogous to speaker identification (SID), in the sense that it is an open-class detection task; the ships present could be other…
With the completion of the IARPA Babel program, it is possible to systematically analyze the performance of speech recognition systems across a wide variety of languages. We select 16 languages from the dataset and compare performance using a deep neural network-based acoustic model. The focus is on…
Spoken term detection, especially of out-of-vocabulary (OOV) keywords, benefits from the use of sub-word systems. We experiment with different language-independent approaches to sub-word unit generation, generating both syllable-like and morpheme-like units, and demonstrate how the performance of sy…
In this paper we describe the 2016 BBN conversational telephone speech keyword spotting system; the culmination of four years of research and development under the IARPA Babel program. The system was constructed in response to the NIST Open Keyword Search (OpenKWS) evaluation of 2016. We present our…
The most common pipelines in keyword spotting involve some kind of speech recognition, which leads to the generation of sets of plausible hypotheses (e.g., word lattices), which are subsequently explored. The case of out-of-vocabulary (OOV) keywords is of special interest, because it requires repres…