言語誤差率を超えて,臨床サンプルにおける自動音声認識の評価:統合失調症スペクトル疾患の研究からの教訓
Sandra Anna Just1, Brita Elvevåg2, Shrankhla Pandey3
1Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway; Department of Psychiatry and Neurosciences, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Germany.
Psychiatry research
|August 24, 2025
まとめ
統合失調症患者の自動音声認識 (ASR) の性能は,症状と起源によって変化し,臨床NLPツールに影響します. 文字の誤差率を超えて 誤差の種類と文脈は メンタルヘルスにおける安全なASRの実施に不可欠です
科学分野:
- コンピュータ言語学
- 精神科
- 医療における人工知能
背景:
- 自動音声認識 (ASR) は,メンタルヘルス研究における自然言語処理 (NLP) に不可欠であり,大規模な研究とスケーラブルな臨床ツールを可能にします.
- 臨床集団におけるASRのパフォーマンスを理解することは,医療環境におけるこれらの技術の安全で効果的な導入に不可欠です.
研究 の 目的:
- 統合失調症スペクトル障害と診断された個人のスピーチサンプルでASRのパフォーマンスを評価する.
- 症状の重症度や人口統計学的変数などのASRの精度に影響を与える要因を特定する.
- 後のNLP分析に対するASR生成トランスクリプトの影響と臨床測定との相関性を評価する.
主な方法:
- 統合失調症スペクトル障害を持つ 50人のスピーチサンプルを分析した.
- 文字エラー率 (WER) の計算
- GloVeの語義的類似性と文数などのNLPメトリックを使用して,ASRトランスクリプトとマニュアルトランスクリプトを比較する.
- ASRトランスクリプトと臨床症状スコアから得られたNLPメトリックの相関評価.
主要な成果:
- 文字誤差率 (WER) は0. 31から0. 58の範囲で,出生国と陽性症状の重さに関連した変動がありました.
- ASRトランスクリプトは,マニュアルトランスクリプトと比較して,GLOVEのセマンティック類似性が高く,文数が少なかった.
- ASRトランスクリプトから得られたNLPメトリックは,マニュアルトランスクリプトより臨床症状スコアとの相関が弱かった.
結論:
- 臨床環境でのASR性能評価は,WERを超えてエラーの種類,意味,文脈を含む必要があります.
- ASRトランスクリプトの違いは,精神衛生研究におけるNLP分析の結果に影響を与える可能性があります.
- この研究は,臨床研究におけるASRの評価の枠組みを提供し,電子医療記録,音声チャットボット,臨床意思決定支援システムの将来の開発と実施を導く.
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