The problem addressed here is that of detecting irregular phonation during conversational speech. While most published work tackles this problem only by focusing on the voiced regions of speech, we focus on detecting irregular phonation without assuming prior knowledge of voiced regions. In addition, we improve the pitch estimation accuracy of a current pitch tracking algorithm in regions of irregular phonation, where most pitch trackers fail to perform well. The algorithm has been tested on the TIMIT and NIST 98 databases. The detection rate for the TIMIT database is 91.8% (17.42% false detections). The detection rate for the NIST 98 database is 91.5% (12.8% false detections). The pitch detection accuracy increased from 95.4% to 98.3% for the TIMIT database, and from 94.8% to 97.4% for the NIST 98 database.