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Fast and Accurate OOV Decoder on High-Level Features
This work proposes a novel approach to out-of-vocabulary (OOV) keyword search
(KWS) task. The proposed approach is based on using high-level features from an
automatic speech recognition (ASR) system, so called phoneme posterior based
(PPB) features, for decoding. These features are obtained by calculating
time-dependent phoneme posterior probabilities from word lattices, followed by
their smoothing. For the PPB features we developed a special novel very fast,
simple and efficient OOV decoder. Experimental results are presented on the
Georgian language from the IARPA Babel Program, which was the test language in
the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum
term weighted value (MTWV) metric and computational speed, for single ASR
systems, the proposed approach significantly outperforms the state-of-the-art
approach based on using in-vocabulary proxies for OOV keywords in the indexed
database. The comparison of the two OOV KWS approaches on the fusion results of
the nine different ASR systems demonstrates that the proposed OOV decoder
outperforms the proxy-based approach in terms of MTWV metric given the
comparable processing speed. Other important advantages of the OOV decoder
include extremely low memory consumption and simplicity of its implementation
and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201
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Real-time decoding of question-and-answer speech dialogue using human cortical activity.
Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate
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