A novel and robust parameter training approach for HMMs under noisy and partial access to states

Cataloged from PDF version of article.This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have partial and noisy access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions. (C) 2013 Elsevier B.V. All rights reserved

Unsupervised spoken keyword spotting and learning of acoustically meaningful units

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-106).The problem of keyword spotting in audio data has been explored for many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world. This thesis addresses this lack-of-annotation problem and presents two completely unsupervised spoken keyword spotting systems that do not require any transcribed data. In the first system, a Gaussian Mixture Model is trained to label speech frames with a Gaussian posteriorgram, without any transcription information. Given several spoken samples of a keyword, a segmental dynamic time warping is used to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all the test utterances. In the second system, to avoid the need for spoken samples, a Joint-Multigram model is used to build a mapping from the keyword text samples to the Gaussian component indices. A keyword instance in the test data can be detected by calculating the similarity score of the Gaussian component index sequences between keyword samples and test utterances. The proposed two systems are evaluated on the TIMIT and MIT Lecture corpus. The result demonstrates the viability and effectiveness of the two systems. Furthermore, encouraged by the success of using unsupervised methods to perform keyword spotting, we present some preliminary investigation on the unsupervised detection of acoustically meaningful units in speech.by Yaodong Zhang.S.M