We developed an algorithm to estimate the depression status from a person's voice signal. In the experiment, we collected voice samples from patients with major depression. In addition, questionnaires concerning the patients' depressed mood were obtained. The voice signals were collected for the subjects' vocalizations of three types of long vowels. Next, acoustic features were calculated based on the speech. Subsequently, an algorithm was developed to estimate the severity of depression, judged by the HAM-D score, from the recorded voice samples. The results indicated that the algorithm performed well at estimating the severity of the HAM-D score using the acoustic features of the long vowels. Consequently, the algorithm also performed well at estimating the depressed mood, thus suggesting the utility of the algorithm for estimating depression conditions based on speech.