Open-Vocabulary Keyword Spotting with Audio and Text Embeddings

Keyword Spotting (KWS) systems allow detecting a set of spoken (pre-defined) keywords. Open-vocabulary KWS systems search for the keywords in the set of word hypotheses generated by an automatic speech recognition (ASR) system which is computationally expensive and, therefore, often implemented as a cloud-based service. Besides, KWS systems could use also word classification algorithms that do not allow easily changing the set of words to be recognized, as the classes have to be defined a priori, even before training the system. In this paper, we propose the implementation of an open-vocabulary ASR-free KWS system based on speech and text encoders that allow matching the computed embeddings in order to spot whether a keyword has been uttered. This approach would allow choosing the set of keywords a posteriori while requiring low computational power. The experiments, performed on two different datasets, show that our method is competitive with other state of the art KWS systems while allowing for a flexibility of configuration and being computationally efficient.


Published in:
INTERSPEECH 2019 - IEEE International Conference on Acoustics, Speech, and Signal Processing
Presented at:
INTERSPEECH 2019 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Graz, Austria
Year:
2019
Keywords:
Laboratories:




 Record created 2019-08-08, last modified 2019-09-05

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)