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This paper investigates the semi-supervised training for deep neural network-based acoustic models (AM). In the conventional self-learning approach, a “seed-AM” is first trained by using a small transcribed data set. Then, a large untranscribed data set is decoded by using the seed-AM to create a transcription, which is finally used to train a new AM on the entire data. Our investigation in this paper focuses on the different approach that uses additional complementary AMs to form a committee of label creation for untranscribed data. Especially, we investigate the case of using heterogeneous neural networks as complementary AMs, and the case of intentional exclusion of the primary seed-AM from the committee, both of which could enhance the chance to find more informative training samples for the seed-AM. We investigated those approaches based on Japanese lecture recognition experiments with 50-hours of transcribed data and 190-hours of untranscribed data. In our experiment, the committee-based approach showed significant improvements in the word error rate, and the best method finally recovered 75.2% of the oracle improvement with full manual transcription, while the conventional self-learning approach recovered only 32.7% of the oracle gain.