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arXiv · 1910.06697

VFNet: A Convolutional Architecture for Accent Classification

Asad Ahmed·Pratham Tangri·Anirban Panda·Dhruv Ramani·Samarjit Karmakar
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Abstract

Understanding accent is an issue which can derail any human-machine interaction. Accent classification makes this task easier by identifying the accent being spoken by a person so that the correct words being spoken can be identified by further processing, since same noises can mean entirely different words in different accents of the same language. In this paper, we present VFNet (Variable Filter Net), a convolutional neural network (CNN) based architecture which captures a hierarchy of features to beat the previous benchmarks of accent classification, through a novel and elegant technique of applying variable filter sizes along the frequency band of the audio utterances.

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BibTeXRIS

Asad Ahmed, Pratham Tangri, Anirban Panda, Dhruv Ramani, Samarjit Karmakar. 2019-10-15. VFNet: A Convolutional Architecture for Accent Classification. https://arxiv.org/abs/1910.06697

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