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Baby Cry Recognition Based on Acoustic Segment Model

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Man-Machine Speech Communication (NCMMSC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1765))

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Abstract

Since babies cannot speak, they can only communicate with the outside world and express their emotions and needs through crying. Considering the variety of reasons why babies cry, it is a challenging task to accurately understand the meaning of baby crying. In this paper, we propose a baby cry recognition method based on acoustic segment model (ASM). Firstly, based on Gaussian mixtures models - hidden Markov models (GMM-HMMs), baby cry recordings are transcribed into ASM sequences composed of ASM units. In this way, different baby cry recordings are segmented in more detail, which can better capture the similarities and differences between acoustic segments. Then, by using latent semantic analysis (LSA), these ASM sequences are converted into feature vectors, and the term-document matrix is obtained. Finally, a simple classifier is adopted to distinguish different types of baby crying. The effectiveness of the proposed method is evaluated on two infant crying databases. The ASM-based approach can achieve higher accuracy compared with the approach based on residual network (ResNet). And through experiments, we analyze the reasons for the better performance of the ASM-based method.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, Anhui, China

    Shuxian Wang, Jun Du & Yajian Wang

Authors
  1. Shuxian Wang
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  2. Jun Du
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  3. Yajian Wang
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Corresponding author

Correspondence to Jun Du .

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Editors and Affiliations

  1. University of Science and Technology of China, Anhui, China

    Ling Zhenhua

  2. Hefei University, Anhui, China

    Gao Jianqing

  3. Shanghai Jiaotong University, Shanghai, China

    Yu Kai

  4. Tsinghua University, Bei**g, China

    Jia Jia

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Wang, S., Du, J., Wang, Y. (2023). Baby Cry Recognition Based on Acoustic Segment Model. In: Zhenhua, L., Jianqing, G., Kai, Y., Jia, J. (eds) Man-Machine Speech Communication. NCMMSC 2022. Communications in Computer and Information Science, vol 1765. Springer, Singapore. https://doi.org/10.1007/978-981-99-2401-1_2

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  • DOI: https://doi.org/10.1007/978-981-99-2401-1_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2400-4

  • Online ISBN: 978-981-99-2401-1

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