The Use of Locally Normalized Cepstral Coefficients (LNCC) to Improve Speaker Recognition Accuracy in Highly Reverberant Rooms

Abstract

We describe the ability of LNCC features (Locally Normalized Cepstral Coefficients) to improve speaker recognition accuracy in highly reverberant environments. We used a realistic test environment, in which we changed the number and nature of reflective surfaces in the room, creating four increasingly reverberant times from approximately 1 to 9 seconds. In this room, we re-recorded reverberated versions of the Yoho speaker verification corpus. The recordings were made using four speaker-to-microphone distances, from 0.32m to 2.56m. Experimental results for a speaker verification task suggest that LNCC features are an attractive alternative to MFCC features under such reverberant conditions, as they were observed to improve verification accuracy compared to baseline MFCC features in all cases where the reverberation time exceeded 1 second or with a greater speaker-microphone distance (i.e. 2.56 m).

Time-Varying Autoregressions for Speaker Verification in Reverberant Conditions

“…In [4], the use of locally normalized cepstral coefficients (LNCCs) was studied. LNCC features modify the conventional MFCC features by using an additional filterbank to perform local normalization in the spectral domain.…”