2019/05/10 by Shauna Revay, Revay, Shauna, Matthew Teschke +1
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1905.04348
openalex publication_date 2019/05/10 · openalex created_date 2019/05/16 · openalex updated_date 2026/07/28
The first step in any voice recognition software is to determine what language a speaker is using, and ideally this process would be automated. The technique described in this paper, language identification for audio spectrograms (LIFAS), uses spectrograms generated from audio signals as inputs to a convolutional neural network (CNN) to be used for language identification. LIFAS requires minimal pre-processing on the audio signals as the spectrograms are generated during each batch as they are input to the network during training. LIFAS utilizes deep learning tools that are shown to be successful on image processing tasks and applies it to audio signal classification. LIFAS performs binary language classification with an accuracy of 97%, and multi-class classification with six languages at an accuracy of 89% on 3.75 second audio clips.