This paper proposes a novel framework for generating action descriptions from human whole body motions and objects to be manipulated. This generation is based on three modules: the first module categorizes human motions and objects; the second module associates the motion and object categories with words; and the third module extracts a sentence structure as word sequences. Human motions and objects to be manipulated are classified into categories in the first module, then words highly relevant to the motion and object categories are generated from the second module, and finally the words are converted into sentences in the form of word sequences by the third module. The motions and objects along with the relations among the motions, objects, and words are parametrized stochastically by the first and second modules. The sentence structures are parametrized from a dataset of word sequences in a dynamical system by the third module. The link of the stochastic representation of the motions, objects, and words with the dynamical representation of the sentences allows for synthesizing sentences descriptive to human actions. We tested our proposed method on synthesizing action descriptions for a human action dataset captured by an RGB-D sensor, and demonstrated its validity.