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We study user-friendly voice interface to consumer electronics and propose a voice activation system that can make speech recognition activated only when voice sounds from legitimate users are detected. The proposed system enables efficient operation of speech recognition in a continuous listening environment without any touch and/or key input.
This paper presents an improved acoustic keyword spotting (KWS) algorithm using a novel confusion garbage model in Mandarin conversational speech. Observing the KWS corpus, we found there are many words with similar pronunciation with predefined keywords, although they have different Chinese characters and different
One of the most important steps in a keyword spotting (KWS) system is a post-processing procedure to compute a confidence measure (CM) for each hypothesized keyword. The CM is commonly estimated by likelihood-based acoustic scores. However durations of the detected keyword, which include useful information, has not
We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model
actual language identification. On our bi-lingual lecture tasks the PPRLM system clearly outperforms the PPR system in various segment length conditions, however at the cost of slower run-time. By using lexical information in the form of keyword spotting, and additional language models we show ways to improve the
acoustic to language constraints, in a single segment-based probabilistic framework. With concept knowledge available, un-recalled keywords are reduced by 37%. Regarding concept extraction and goal identification, the proposed method is shown to be superior to the baseline system. Concepts are extracted at more than 80% of
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