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Spoken Language Identification Using ConvNets

  • Sarthak
  • Shikhar ShuklaEmail author
  • Govind Mittal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)

Abstract

Language Identification (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a language is present or an explicit one where text is available with its corresponding transcript. This paper focuses on an implicit approach due to the absence of transcriptive data. This paper benchmarks existing models and proposes a new attention based model for language identification which uses log-Mel spectrogram images as input. We also present the effectiveness of raw waveforms as features to neural network models for LI tasks. For training and evaluation of models, we classified six languages (English, French, German, Spanish, Russian and Italian) with an accuracy of 95.4% and four languages (English, French, German, Spanish) with an accuracy of 96.3% obtained from the VoxForge dataset. This approach can further be scaled to incorporate more languages.

Keywords

Language Identification Raw waveform Convolutional Neural Networks Machine learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Analytics QuotientBangaloreIndia
  2. 2.Samsung R&D Institute India-BangaloreBangaloreIndia
  3. 3.Birla Institute of Technology and SciencePilaniIndia

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