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Automatic Speech-Based Smoking Status Identification

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Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 508))

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Abstract

Identifying the smoking status of a speaker from speech has a range of applications including smoking status validation, smoking cessation tracking, and speaker profiling. Previous research on smoking status identification mainly focuses on employing the speaker's low-level acoustic features such as fundamental frequency (F0), jitter, and shimmer. However, the use of high-level acoustic features, such as Mel Frequency Cepstral Coefficients (MFCC) and filter bank (Fbank) for smoking status identification, has rarely been explored. In this study, we utilise both high-level acoustic features (i.e., MFCC, Fbank) and low-level acoustic features (i.e., F0, jitter, shimmer) for smoking status identification. Furthermore, we propose a deep neural network approach for smoking status identification by employing ResNet along with these acoustic features. We also explore a data augmentation technique for smoking status identification to further improve the performance. Finally, we present a comparison of identification accuracy results for each feature settings, and obtain the best accuracy of 82.3%, a relative improvement of 12.7% and 29.8% on the initial audio classification approach and rule-based approach, respectively.

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Authors and Affiliations

  1. School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand

    Zhizhong Ma, Satwinder Singh, Yuanhang Qiu, Feng Hou & Ruili Wang

  2. National Institute for Health Innovation, University of Auckland, Auckland, New Zealand

    Christopher Bullen & Joanna Ting Wai Chu

Authors
  1. Zhizhong Ma
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  2. Satwinder Singh
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  3. Yuanhang Qiu
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  4. Feng Hou
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  5. Ruili Wang
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  6. Christopher Bullen
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  7. Joanna Ting Wai Chu
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Corresponding author

Correspondence to Feng Hou .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Ma, Z. et al. (2022). Automatic Speech-Based Smoking Status Identification. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 508. Springer, Cham. https://doi.org/10.1007/978-3-031-10467-1_11

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