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咳音に含まれるジェンダーの解読：Transformerベースの分析

Linh He1, Haomiao Li2, Siyan Wang2

  • 1Sean Parker Institute for the Voice, Department of Otolaryngology - Head & Neck Surgery, Weill Cornell Medical College, New York, New York, USA.

The Laryngoscope
|January 27, 2026
PubMed
まとめ
この要約は機械生成です。

咳のアコースティックにはジェンダー固有の特徴が含まれており、AIモデルで検出可能です。これらの発見は、非音声の違いを特定することにより、ジェンダー肯定的な音声ケアに役立つ可能性があります。

キーワード:
AI咳ジェンダー

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科学分野:

  • 喉頭学; 音響分析; 人工知能

背景:

  • ピッチや音量などの音声特性は、ジェンダーの認識に影響を与えます。咳のような非音声は、ジェンダーの違いに関してあまり理解されていません。この知識のギャップは、咳の変調が困難であるため、ジェンダー肯定的な音声ケアに影響を与えます。

研究 の 目的:

  • 咳音におけるジェンダーベースのアコースティックな違いを調査すること。ジェンダー分類のための主要な咳の特徴を特定するために、自己注意機構を備えたTransformerモデルを利用すること。

主な方法:

  • Coswaraデータセットから327件の咳の録音（男性154件、女性173件）を分析しました。前処理（リサンプリング、無音除去、正規化、トリミング）後に音声分析のためにHuBERT Transformerモデルを適用しました。標準化平均差（SMD）スクリーニングを使用してジェンダーバランスを検証しました。

主要な成果:

  • Transformerモデルは、咳の連続音からのジェンダー分類において、精度84.0%、F1スコア0.8462を達成しました。単一の咳サンプルのみを使用した場合は、分類精度が低く（71.4%）なりました。注意機構の可視化により、爆発的な咳のフェーズがジェンダー識別の鍵となることが強調されました。

結論:

  • 咳音には、Transformerモデルによって識別可能なジェンダー識別可能なアコースティック特徴が存在します。特定の咳のフェーズには、ジェンダー分類のための生理学的に関連のある情報が含まれています。この発見は、非音声の生成のためのジェンダー肯定的な介入に情報を提供する可能性があります。