This work explores the timing of very short utterances in conversations, as well as the effects of excluding intervals adjacent to such utterances from distributions of betweenspeaker interval durations. The results show that very short utterances are more precisely timed to the preceding utterance than longer utterances in terms of a smaller variance and a larger proportion of no-gap-no-overla...

This paper presents a method for reducing the effort of transcribing user utterances to develop language models for conversational speech recognition when a small number of transcribed and a large number of untranscribed utterances are available. The recognition hypotheses for untranscribed utterances are classified according to their confidence scores such that hypotheses with high confidence ...

This paper presents an effective technique for clustering speech utterances based on their associated speaker. In attempts to determine which utterances are from the same speakers, a prerequisite is to measure the similarity of voice characteristics between utterances. Since the vast majority of existing methods evaluate the inter-utterance similarity by taking only the information from the spe...

This paper discusses how to build a system that can engage in a mixed-initiative human-machine spoken dialogue in which system utterances sometimes overlap with user utterances and vice versa. In the method, a module that incrementally understands user utterances and another module that incrementally generates system utterances work in parallel, and the timing of taking and releasing the dialog...

The DUET blind source separation algorithm can demix an arbitrary number of speech signals usingM = 2 anechoic mixtures of the signals. DUET however is limited in that it relies upon source signals which are mixed in an anechoic environment and which are sufficiently sparse such that it is assumed that only one source is active at a given time frequency point. The DUET-ESPRIT (DESPRIT) blind so...