Furthermore, as the conversational agent gathers information about the user, storing this information on the web, it becomes available to the personal assistant and the intelligent environment. Thus each layer of the whole system is aiding the others. Unfortunately, the recognition accuracy of this combination is, at present, very poor. With sufficient training, these systems can build predictive models that make it possible to guess, with reasonable accuracy, how a spoken word should be recognised. Unlike dictation, utterances in a conversation often consist of incomplete or grammatically incorrect sentences.
Many utterances are difficult to understand without knowledge of the context of the conversation.
The research proposed here will not deal directly with speech recognition. However, the key to a successful speech recognition system is having enough foreknowledge about the utterance that its possible interpretations can be limited to a small number. Thus, many dictation systems use training sessions on known texts to build Hidden Markov Models to predict phoneme and word sequences. These kinds of models do not work well for conversations, for the reasons given above. Therefore, we need some other means of constraining the possible interpretations of utterances.