Cognitive component analysis (COCA) is defined as the process of unsupervised grouping of data such that the resulting group structure is well-aligned with that resulting from human cognitive activity. In this paper we address COCA in the context short time sound features, finding phonemes which are the smallest contrastive unit in the sound system of a language. Generalizable components were found deriving from phonemes based on homomorphic filtering features with basic time scale (20 msec). We sparsified the features based on energy as a preprocessing means to eliminate the intrinsic noise. Independent component analysis was compared with latent semantic indexing, and was demonstrated to be a more appropriate model in COCA.
|Title of host publication||International Conference on Acoustics, Speech and Signal Processing (ICASSP'06)|
|Publication status||Published - 2006|
|Event||IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) - Toulouse, France|
Duration: 14 May 2006 → 19 May 2006
|Conference||IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)|
|Period||14/05/2006 → 19/05/2006|