https://www.cs.ubc.ca/~murphyk/Teaching/CS540-Spring10/projects/Yu-hsmm09.pdf

Hidden semi-Markov models

A method and apparatus is provided for using multiple feature streams in speech recognition. In the method and apparatus, a feature extractor generates at least two feature vectors for a segment of an input signal. A decoder then generates a path score that is indicative of the probability that a word is represented by the input signal. The path score is generated by selecting the best feature vector to use for each segment. For each segment, the corresponding part in the path score for that segment is based in part on a chosen segment score that is selected from a group of at least two segment scores. The segment scores each represent a separate probability that a particular segment unit (e.g. senone, phoneme, diphone, triphone, or word) appears in that segment of the input signal. Although each segment score in the group relates to the same segment unit, the scores are based on different feature vectors for the segment.

Speech recognition method and apparatus utilizing multiple feature streams

The Web has become a primary source of product information for online consumers. As such, basic questions regarding how consumers approach the search process need to be understood and measured if marketers and website developers are to identify patterns or trends in consumer information search strategies. To observe and measure changes in consumer search behaviour, it is necessary to establish benchmarks that can, at a later date, be compared with subsequent observation measurements. This study recorded the search activities of 37 subjects as they searched for information regarding the purchase of two products: one where they had expert product knowledge and one where they had novice product knowledge. Significance was found for particular site types used and, more importantly, in patterns of site type usage. The methodology and benchmarks offer researchers a means of tracking changes in consumer search behaviour on the Web. Copyright © 2003 Henry Stewart Publications.

Web metrics: measuring patterns in online shopping

Recent work on Conditional Random Fields (CRFs) has demonstrated the need for regularisation when applying these models to real-world NLP data sets. Conventional approaches to regularising CRFs has focused on using a Gaussian prior over the model parameters. In this paper we explore other possibilities for CRF regularisation. We examine alternative choices of prior distribution and we relax the usual simplifying assumptions made with the use of a prior, such as constant hyperparameter values across features. In addition, we contrast the effectiveness of priors with an alternative, parameter-free approach. Specifically, we employ logarithmic opinion pools (LOPs). Our results show that a LOP of CRFs can outperform a standard unregularised CRF and attain a performance level close to that of a regularised CRF, without the need for intensive hyperparameter search.

/pdf/regularisation-techniques-for-conditional-random-fields-35k9obh5oj.pdf

Regularisation techniques for conditional random fields: parameterised versus parameter-free

In recent years, several alternative acoustic models have been proposed that attempt to represent trends or correlation of observations over time. These models, which can be broadly classified as segment models, are surveyed in this chapter and presented in a general probabilistic framework that includes the hidden Markov model (HMM) as a special case. The overview gives options for modeling assumptions in terms of correlation structure and parameter tying and outlines the extensions to HMM recognition and training algorithms needed to handle segment models.

Segmental Acoustic Modeling for Speech Recognition

There are two major approaches to speech recognition: frame-based and segment-based approach. The frame-based approach, e.g. HMM, assumes a statistical independence and an identical distribution of the observation in each state. In addition it incorporates weak duration constraints. The segment-based approach is computational expensive and rough modelling easily occurs if not much 'templates' are stored. This paper presents a new framework to incorporate the segmental feature and the segmental model in a unified way into frame-based HMM to exploit the advantage of both methods. In the modified Viterbi algorithm, frame-based information prunes out the most probable path at each segment level to which the segmental model can be applied with dramatically reduced computational load; at the same time, the segmental score refines the score obtained by the frame-based model at each level. In this way, the best path found in the end, by the Viterbi algorithm, is optimal.

A unified way in incorporating segmental feature and segmental model into HMM

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A unified way in incorporating segmental feature and segmental model into HMM