A language-identification inspired method for spontaneous speech detection

Most of spontaneous speech detection systems relies on dis-fluency analysis or on combination of acoustic and linguistic features. This paper presents a method that considers spontaneous speech as a specific language, which could be identified by using language-recognition methods, such as shifted delta cepstrum parameters, dimensionality reduction by linear dis-criminant analysis and factor-analysis based filtering process. Experiments are conducted on the French EPAC corpus. On a 3 spontaneity-level task, this approach obtains a relative gain of about 22% of identification rates, in comparison to the classical MFCC/GMM technique. Then, we combine these techniques to others previously proposed for spontaneous speech detection. Finally, the proposed system obtains a recognition rate of 65% on high spontaneous speech segments.

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