Recently, the accuracy of speaker-independent speech recognition has been remarkably improved by use of stochastic modeling of speech. However, there has been very little research on orally-challenged people, such as those with speech impediments. Therefore we have tried to build the acoustic model for a person with articulation disorders. The articulation of the speech tends to become unstable due to strain of a muscle and that causes degradation of speech recognition. In this paper, we focus on the fact that recognition rate of a person with an articulation disorder decreases compared with that of a physically unimpaired person, expecially in speech recognition using dynamic features only. Therefore, we use multiple acoustic frames as an acoustic feature to solve this problem. Its effectiveness is confirmed by word recognition experiments.%音声認識技術は現在，様々な環境下や場面において使用される機会が増加している．しかし，言語障害などの障害者を対象としたものは非常に少ない．本稿では，アテトーゼ型脳性麻痺による構音障害者の音声認識の検討を行う．アテトーゼ型の構音障害者の発話スタイルは，筋肉の緊張のため健常者と大きく異なり不安定であるため，特定話者モデルでの音声認識には限界がある．特に構音障害者の動的特徴量（デルタケプストラム）の認識精度は健常者に比べて大きく低下する．これに対し本稿では，動的特徴量の代わりに，デルタケプストラム係数のセグメント特徴量を用いることで構音障害者の音声認識精度の改善を試み，その有効性を示す．
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