This notion aligns with the observation of (Spitkovsky et al., 2010) who criticize EM for frittering away too much probability mass on unimportant outputs while showing that hard EM does much better in PCFG parsing.
they empirically show that when initialized with a "good" set of parameters obtained by supervised learning, EM drifts away (thus losing accuracy) much farther than hard-EM.