Perceptual evaluation is still the most common method in clinical practice for the diagnosing and the following of the condition progression of people with speech disorders. Many automatic approaches were proposed to provide objective tools to deal with speech disorders and help professionals in the severity evaluation of speech impairments. This paper investigates an automatic phone-based anomaly detection approach implying an automatic text-constrained phone alignment. Here, anomalies are related to speech segments, for which an unexpected acoustic pattern is observed, compared with a normal speech production. This objective tool is applied to French dysarthric speech recordings produced by patients suffering from four different pathologies. The behavior of the anomaly detection approach is studied according to the precision of the automatic phone alignment. Faced with the difficulties of having a gold standard reference, especially for the phone-based anomaly annotation , this behavior is observed on both annotated and non-annotated corpora. As expected, alignment errors (large shifts compared with a manual segmentation) lead to a large amount of anomalies automatically detected. However, about 50% of correctly detected anomalies are not related to alignment errors. This behavior shows that the automatic approach is able to catch irregular acoustic patterns of phones.