  This paper introduces a novel approach for streaming openvocabulary keyword
spotting (KWS) with text-based keyword enrollment. For every input frame, the
proposed method finds the optimal alignment ending at the frame using
connectionist temporal classification (CTC) and aggregates the frame-level
acoustic embedding (AE) to obtain higher-level (i.e., character, word, or
phrase) AE that aligns with the text embedding (TE) of the target keyword text.
After that, we calculate the similarity of the aggregated AE and the TE. To the
best of our knowledge, this is the first attempt to dynamically align the audio
and the keyword text on-the-fly to attain the joint audio-text embedding for
KWS. Despite operating in a streaming fashion, our approach achieves
competitive performance on the LibriPhrase dataset compared to the
non-streaming methods with a mere 155K model parameters and a decoding
algorithm with time complexity O(U), where U is the length of the target
keyword at inference time.
