This paper summarizes our recent efforts for building a Turkish Broadcast News transcription and retrieval system. The agglutinative nature of Turkish leads to a high number of out-of-vocabulary (OOV) words which in turn lower automatic speech recognition (ASR) accuracy. This situation compromises the performance of speech retrieval systems based on ASR output. Therefore using a word-based ASR is not adequate for transcribing speech in Turkish. To alleviate this problem, various sub-word-based recognition units are utilized. These units solve the OOV problem with moderate size vocabularies and perform even better than a 500 K word vocabulary as far as recognition accuracy is concerned. As a novel approach, the interaction between recognition units, words and sub-words, and discriminative training is explored. Sub-word models benefit from discriminative training more than word models do, especially in the discriminative language modeling framework. For speech retrieval, a spoken term detection system based on automata indexation is utilized. As with transcription, retrieval performance is measured under various schemes incorporating words and sub-words. Best results are obtained using a cascade of word and sub-word indexes together with term-specific thresholding.