On the Importance of Analytic Phase of Speech Signals in Spoken Language Recognition

Abstract

In this paper, we study the role of long-time analytic phase of speech signals in spoken language recognition (SLR) and employ a set of features termed as instantaneous frequency cepstral coefficients (IFCC). We extract IFCC from long-time analytic phase, in an effort to capture long range acoustic features from speech signals. These features are used in combination with the traditional shifted delta cepstral coefficients (SDCC) for SLR. As the SDCC are extracted from spectral magnitude and IFCC are from analytic phase, they characterize long-time information of speech in different ways. The experiments conducted with NIST LRE 2017 task reveals the complementary effects of IFCC features to SDCC and deep bottleneck (DBN) features. The fusion of IFCC with SDCC/DBN features delivered relative improvements of 23.23% and 16.78% in average equal error rate over the SDCC and DBN features, respectively, indicating the benefits of information from analytic phase in SLR.