Query word retrieval from continuous speech using GMM posteriorgrams

The objective of this work is to study the issues involved in building an automatic query word retrieval system for broadcast news in an unsupervised framework, i.e., without using any labelled speech data. In the absence of labelled data, sequence of feature-vectors extracted from the query word have to be matched with those extracted from the test utterance. This is a non-trivial task, as typical feature-vectors like Mel-frequency cepstral coefficients (MFCC) carry both speech-specific and speaker-specific information. In this work, we have employed Gaussian mixture models (GMM) to extract speaker-independent features from the speech signal. Gaussian mixture model, trained on a large amount of speech data, is used to derive posterior features for each frame of speech signal. The sequence of posterior features are matched using dynamic time-warping algorithm to detect the presence of query word in the test utterance. The performance of the proposed method is evaluated on Telugu broadcast news database. It is observed that the posterior features extracted from GMM are better suited for query word retrieval compared to the MFCC features.