A swallowing diagnosis apparatus comprising: a sound detection part configured to detect sound of a larynx portion; a respiration detection part configured to detect respiration; a swallowing estimation part configured to estimate that swallowing has occurred, on the basis of matching realized between a biogenic sound generation interval for which a feature quantity satisfying a swallowing estimation condition has been obtained in sound information outputted from the sound detection part, and an apneic interval for which respiration has not been detected for longer than or equal to a predetermined time in respiration information outputted from the respiration detection part; a first swallowing determination part configured to detect, from the respiration information, respiratory phases before and after a period in which swallowing has been estimated as having occurred by the swallowing estimation part, and determine whether or not there is an aspiration risk in the swallowing on the basis of the detected respiratory phases; a second swallowing determination part configured to extract reference information including the sound information and the respiration information in a predetermined period including the period in which swallowing has been estimated as having occurred by the swallowing estimation part, obtain a feature quantity from the extracted reference information, and perform a machine learning process on the obtained feature quantity to determine whether or not there is a possibility of dysphagia in the swallowing using a stored teaching data group or using stored data specifying a boundary which separates dysphagia positive from dysphagia negative; and a display control part configured to cause a display to display a determination result obtained by the first swallowing determination part and a determination result obtained by the second swallowing determination part. a sound detection part configured to detect sound of a larynx portion; a respiration detection part configured to detect respiration; a swallowing estimation part configured to estimate that swallowing has occurred, on the basis of matching realized between a biogenic sound generation interval for which a feature quantity satisfying a swallowing estimation condition has been obtained in sound information outputted from the sound detection part, and an apneic interval for which respiration has not been detected for longer than or equal to a predetermined time in respiration information outputted from the respiration detection part; a first swallowing determination part configured to detect, from the respiration information, respiratory phases before and after a period in which swallowing has been estimated as having occurred by the swallowing estimation part, and determine whether or not there is an aspiration risk in the swallowing on the basis of the detected respiratory phases; detect, from the respiration information, respiratory phases before and after a period in which swallowing has been estimated as having occurred by the swallowing estimation part, and determine whether or not there is an aspiration risk in the swallowing on the basis of the detected respiratory phases; a second swallowing determination part configured to extract reference information including the sound information and the respiration information in a predetermined period including the period in which swallowing has been estimated as having occurred by the swallowing estimation part, obtain a feature quantity from the extracted reference information, and perform a machine learning process on the obtained feature quantity to determine whether or not there is a possibility of dysphagia in the swallowing using a stored teaching data group or using stored data specifying a boundary which separates dysphagia positive from dysphagia negative; and extract reference information including the sound information and the respiration information in a predetermined period including the period in which swallowing has been estimated as having occurred by the swallowing estimation part, obtain a feature quantity from the extracted reference information, and perform a machine learning process on the obtained feature quantity to determine whether or not there is a possibility of dysphagia in the swallowing using a stored teaching data group or using stored data specifying a boundary which separates dysphagia positive from dysphagia negative; and a display control part configured to cause a display to display a determination result obtained by the first swallowing determination part and a determination result obtained by the second swallowing determination part.