Automatic speaker and age identification of children from raw speech using sincNet over ERB scale

This paper presents the newly developed non-native children’s English speech (NNCES) corpus to reveal the findings of automatic speaker and age recognition from raw speech. Convolutional neural networks (CNN), which have the ability to learn low-level speech representations, can be fed directly with raw speech signals instead of using traditional hand-crafted features. Moreover, the filters that were learned using standard CNNs appeared to be noisy because they consider all elements of each filter. In contrast, sincNet can be able to generate more meaningful filters simply by replacing the first convolutional layer by a sinc-layer in standard CNNs. The low and high cutoff frequencies of the rectangular band-pass filter are the only parameters that can be learned in sincNet, which has the potential to extract significant speech cues from the speaker, such as pitch and formants. In this work, the sincNet model is significantly changed by switching from baseline Mel scale initializations to equivalent rectangular bandwidth (ERB) initializations, which has the added benefit of allocating additional filters in the lower region of the spectrum. Additionally, it needs to be highlighted that the novel sincNet model is well suited to identify the age of the children. The investigations on both read and spontaneous speech tasks in speaker identification, gender independent & dependent age-group identification of children outperform the baseline models with varying relative improvements in terms of accuracy.