Similarity matrices applied to differentiate between Lombard and natural speech

Abstract

The study aims to present a method of evaluating speech quality in noise and interference conditions based on similarity matrices. For that purpose, sets of words recorded in the presence of disturbing signals along with their clean counterparts are used. First, it is checked to what extent a correct alignment of signals with and without the Lombard effect is important when self-similarity matrices are utilized in the process of feature discerning. Then, self-similarity matrices based on the acoustic parameters concerning speech prosody and alignment are built. Next, a correlation check is performed for feature redundancy. Based on a reduced set of parameters, 2D maps of acoustic features are created for visualization purposes. This is also performed as the cross-check between different languages with and without Lombard speech. Analyses performed shown that self-similarity matrices may be applied for differentiating speech modified by the Lombard effect and non-Lombard utterances. This research is funded by the European Social Fund under the No 09.3.3-LMT-K-712 “Development of Competences of Scientists, other Researchers and Students through Practical Research Activities” measure.