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We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior
In recent years, so-called, “end-to-end” speech recognition systems have emerged as viable alternatives to traditional ASR frameworks. Keyword search, localizing an orthographic query in a speech corpus, is typically performed by using automatic speech recognition (ASR) to generate an index. Previous
This paper presents a novel method for deriving patterns for classification of speech sounds. In contrast to conventional methods that attempt to capture time-frequency patterns as represented by spectral envelopes or peaks, our method captures patterns of high-energy tracks, or seams, of maximum “whiteness” across frequency in spectrograms. Our hypothesis is that these seams could potentially carry...
-by-example), which can retrieve morphologically similar words that have matching sub-words. Further, to enable query-by-keyword, we build an automated scheme to generate labeled exemplars for characters and character n-grams, from unconstrained handwritten documents. We pose this problem as one of weakly-supervised
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