Native language identification for Indian-speakers by an ensemble of phoneme-specific, and text-independent convolutions.

• The proposed model fuses hierarchical CNNs, applied to vowel segments and the complete utterances of speech, for native language identification. • The model achieves up to 83.6% average accuracy, over 80–20% train-test ratios from the dataset comprising five native Indian languages with 55 speakers each. • The paper presents classification results for short and long durations of speech and individually for all ARPABET vowel segments. • The model effectively employs Low-Pass-Filtering based speech augmentation to improve the classification accuracy. Identifying the social background of an unknown speaker by speech accent has multiple applications including in forensic profiling and adaptation of speech recognition. The most effective accent classification models based on phoneme pronunciation require the presence of certain phonemes in the test speech and hence, are applicable only for a longer duration of test samples. On the other hand, the text-independent classifiers disregard the phoneme and linguistic information completely. This paper proposes an ensemble of convolutional neural networks for phoneme-based short-term and text-independent long-term classification of speech regarding speaker background profiling. The model is evaluated by classifying the native language of Indian speakers by their English speech. Both the classifiers within the ensemble complement each other positively; to give higher classification accuracy as compared to classification accuracies obtained from the individual classifiers. Low-pass filtering based speech augmentation has been proven to further improve the classification performance and average accuracy, with up to 79% and 73.7% accuracies achieved for speaker-level and sentence-level tests, respectively. [ABSTRACT FROM AUTHOR]