In LNBNN, we use both SIFT and MFCC features extracted from spectrogram images of speech signals.
Prior work [32,33] demonstrated that the structural similarity index (SSIM) [35] could be used to discriminate between reference and degraded images of speech to predict intelligibility.
In this paper, we propose an approach for speech classification using Scale-invariant Feature Transform (SIFT) features on spectrogram images of speech signal combination with Local naïve Bayes nearest neighbor.
The opposite of "literally" is "figuratively", so if you use "literally" and you're not trying to distinguish what you just said from a figure of speech, an image or a simile, you sound like you spent the afternoon huffing gold-fleck paint.
Some recent researches already have extracted speech feature from spectrogram image of a speech signal for audio retrieval system [49, 50].
Somewhere, deep in my jaded heart, the America I want to believe in still lives, and it has a very specific image: Freedom of Speech, a Norman Rockwell oil painting commissioned in 1943 by The Saturday Evening Post.
"Devil?" "Figure of speech.
Figures 1, 2, 3, 4 and 5 are spectrogram images of some speech signals.
Wundt's American counterpart, and contemporary, William James, took a not dissimilar view, although he was careful to acknowledge that in some people the "thought stuff," as he called it, might consist not so much of visual imagery as of imagery of other modes, especially the "verbal images" of inner speech (James, 1890 ch. 18).
