We present our work on training speaker embeddings, especially effective for speaker diarization. For various speaker recognition tasks, extracting speaker embeddings using Deep Neural Networks (DNNs) has become major methods. These embeddings are generally trained to be discriminate speakers and be robust with respect to different acoustic conditions. In speaker diarization, however, the acoustic conditions can be used as consistent information for discriminating speakers. Such information can include the distances to a microphone in a meeting, or the channels for each speaker in telephone conversation recorded in monaural. Hence, the proposed speaker-embedding network leverages differences in acoustic conditions to train effective speaker embeddings for speaker diarization. The information on acoustic conditions can be anything that contributes to distinguishing between recording environments; for example, we explore using i-vectors. Experiments conducted on a practical diarization system demonstrated that the proposed embeddings significantly improve performance over embeddings without information on acoustic conditions.