By using speech recognition technology, Computer Assisted Language Learning (CALL) programs can provide learners with opportunities to practise speaking in the target language and develop their oral language skills. This research is a contribution to the emerging and innovative area of speech-enabled CALL applications. It describes a CALL application, SPELL (Spoken Electronic Language Learning), which integrates software for speaker independent continuous speech recognition with embodied virtual agents and virtual worlds to create an immersive environment in which learners can converse in the target language in contextualized scenarios. The design of the program is based on a communicative approach to second language acquisition which posits that learning activities should give learners opportunities to communicate in the target language in meaningful contexts. In applying a communicative approach to the design of a CALL program, the speech recogniser is programmed to allow a variety of responses form the learner and to recognise grammatical and ungrammatical utterances so that the learner can receive relevant and immediate feedback to their utterance. Feedback takes two key forms: reformations, where the system repeats or reformulates the agent’s initial speech, and recasts, where the system repeats the learner’s utterance, implicitly correcting any errors. This research claims that speech-enabled CALL systems which employ an open-ended approach to the recognition grammars and which adapt a communicative approach are usable, engaging and motivating conversational tools for language learners. In addition, by employing implicit feedback strategies in the design, speech recognition errors can be mitigated such that interactions between learners and embodied virtual agents can proceed while providing learners with valuable target language input during the interactions. These claims are based on a series of three empirical studies conducted with end users of the system.