Zero-resource audio-only spoken term detection based on a combination of template matching techniques

Armando Muscariello (1) , Guillaume Gravier (1) , Frédéric Bimbot (1)

1 METISS - Speech and sound data modeling and processing

Spoken term detection is a well-known information retrieval task that seeks to extract contentful information from audio by locating occurrences of known query words of interest. This paper describes a zero-resource approach to such task based on pattern matching of spoken term queries at the acoustic level. The template matching module comprises the cascade of a segmental variant of dynamic time warping and a self-similarity matrix comparison to further improve robustness to speech variability. This solution notably differs from more traditional train and test methods that, while shown to be very accurate, rely upon the availability of large amounts of linguistic resources. We evaluate our framework on different parameterizations of the speech templates: raw MFCC features and Gaussian posteriorgrams, French and English phonetic posteriorgrams output by two different state of the art phoneme recognizers.

Domains: Signal and Image Processing; Signal and Image processing

Armando Muscariello, Guillaume Gravier, Frédéric Bimbot. Zero-resource audio-only spoken term detection based on a combination of template matching techniques. INTERSPEECH 2011: 12th Annual Conference of the International Speech Communication Association, Aug 2011, Florence, Italy. ⟨inria-00597907⟩