This paper analyses the short utterance probabilistic linear discriminant analysis (PLDA) speaker verification with utterance partitioning and short utterance variance (SUV) modelling approaches. Experimental studies have found that instead of using single long-utterance as enrolment data, if long enrolledutterance is partitioned into multiple short utterances and average of short utterance i-v...

This paper analyses the short utterance probabilistic linear discriminant analysis (PLDA) speaker verification with utterance partitioning and short utterance variance (SUV) modelling approaches. Experimental studies have found that instead of using single long-utterance as enrolment data, if long enrolledutterance is partitioned into multiple short utterances and average of short utterance i-v...

Utterance rewriting aims to recover coreferences and omitted information from the latest turn of a multi-turn dialogue. Recently, methods that tag rather than linearly generate sequences have proven stronger in both in- out-of-domain settings. This is due tagger's smaller search space as it can only copy tokens dialogue context. However, these may suffer low coverage when phrases must be added ...

Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

In a conversational context, user converses with system through sequence of natural-language questions, i.e., utterances. Starting from given subject, the conversation evolves sequences utterances and replies. The retrieval documents relevant to an utterance is difficult due informal use natural language in speech complexity understanding semantic context coming previous We adopt 2019 TREC Conv...