A New Method Towards Speech Files Local Features Investigation
This work addresses the challenge of improving speech analysis for language identification, but appears incremental as it builds on existing feature extraction concepts.
The authors tackled the problem of detecting local features in speech files that traditional methods miss, by approximating signals with finite sets and analyzing vector distributions, and applied it to language distinction with a neural network, achieving unspecified results.
There are a few reasons for the recent increased interest in the study of local features of speech files. It is stated that many essential features of the speaker language used can appear in the form of the speech signal. The traditional instruments - short Fourier transform, wavelet transform, Hadamard transforms, autocorrelation, and the like can detect not all particular properties of the language. In this paper, we suggest a new approach to the exploration of such properties. The source signal is approximated by a new one that has its values taken from a finite set. Then we construct a new sequence of vectors of a fixed size on the base of those approximations. Examination of the distribution of the produced vectors provides a new method for a description of speech files local characteristics. Finally, the developed technique is applied to the problem of the automatic distinguishing of two known languages used in speech files. For this purpose, a simple neural net is consumed.