Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Spoken term detection is a ll-known information retrieval task that seeks to extract contentful information from audio by locating occurrences of known query words of interest.  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.  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.
['Armando Muscariello', 'Guillaume Gravier', 'Frédéric Bimbot']
I am writing a research paper on a idea Zero-resource audio-only spoken term detection based on a combination of template matching techniques
[{"from": "human", "value": "I am writing a research paper on a idea Zero-resource audio-only spoken term detection based on a combination of template matching techniques"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Spoken term detection is a ll-known information retrieval task that seeks to extract contentful information from audio by locating occurrences of known query words of interest.  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.  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."}]