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shortcomings of the present E2E KWS methods, there is a lack of an open-source toolkit like Wenet [14] in the field of small-footprint KWS to bridge the gap between research and production. There are several good speech-processing toolk-its, e.g. Kaldi [15], Fairseq [16] and Honk [17], that have im-plemented KWS as part of their features.
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The typical KWS pipeline is based on a lattice obtained from a Large Vocabulary Continuous Speech Recognition (LVCSR) system, wherein a neural acoustic model (AM) and a word-based language model (LM) are both applied to generate the word-level lattices by decoding . Consequently, KWS can be performed mainly on lattices.
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Accurate spoken term detection (STD), also named keyword searching (KWS), is a vital downstream application of automatic speech recognition (ASR). For information …
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Since KWS system is always-on, it should have very low power consumption to maximize battery life. On the other hand, the KWS system should detect the keywords with high accuracy and low latency, for best user experience. These conflicting system requirements make KWS an active area of research ever since its inception over 50 years ago [4].
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