An Effective Speaker Clustering Method using UBM and Ultra-Short Training Utterances

Abstract

The same speech sounds (phones) produced by different speakers can sometimes exhibit significant differences. Therefore, it is essential to use algorithms compensating these differences in ASR systems. Speaker clustering is an attractive solution to the compensation problem, as it does not require long utterances or high computational effort at the recognition stage. The report proposes a clustering method based solely on adaptation of UBM model weights. This solution has turned out to be effective even when using a very short utterance. The obtained improvement of frame recognition quality measured by means of frame error rate is over 5%.It is noteworthy that this improvement concerns all vowels, even though the clustering discussed in this report was based only on the phoneme a. This indicates a strong correlation between the articulation of different vowels, which is probably related to the size of the vocal tract.

Speaker Model Clustering to Construct Background Models for Speaker Verification

“…However, there is a tradeoff between the identification rate and identification time, since not all the mixtures are scored, or not all the speakers' models are considered. Speaker clustering method is also used to compensate speaker-related effects in speech recognition recently (Hossa, Makowski, 2016).…”