Applying feature reduction analysis to a PPRLM-multiple Gaussian language identification system

This paper presents the application of a feature selection technique such as LDA to a language identification (LID) system. The baseline system consists of a PPRLM module followed by a multiple-Gaussian classifier. This classifier makes use of acoustic scores and duration features of each input utterance. We applied a dimension reduction of the feature space in order to achieve a faster and easier-trainable system. We imputed missing values of our vectors before projecting them on the new space. Our experiments show a very low performance reduction due to the dimension reduction approach. Using a single dimension projection the error rates we have obtained are about 8.73% taking into account the 22 most significant features

n-gram Frequency Ranking with additional sources of information in a multiple-Gaussian classifier for Language Identification

We present new results of our n-gram frequency ranking used for language identification. We use a Parallel phone recognizer (as in PPRLM), but instead of the language model, we create a ranking with the most frequent n-grams. Then we compute the distance between the input sentence ranking and each language ranking, based on the difference in relative positions for each n-gram. The objective of this ranking is to model reliably a longer span than PPRLM. This approach overcomes PPRLM (15% relative improvement) due to the inclusion of 4-gram and 5-gram in the classifier. We will also see that the combination of this technique with other sources of information (feature vectors in our classifier) is also advantageous over PPRLM, showing also a detailed analysis of the relevance of these sources and a simple feature selection technique to cope with long feature vectors. The test database has been significantly increased using cross-fold validation, so comparisons are now more reliable

Acoustic Scene Classification

This work was supported by the Centre for Digital Music Platform (grant EP/K009559/1) and a Leadership Fellowship (EP/G007144/1) both from the United Kingdom Engineering and Physical Sciences Research Council