Clustering Wide-Contexts and HMM Topologies
for Spontaneous Speech Recognition


Izhak Shafran


Abstract

In most speech recognition systems today, all the acoustic variation associated with a phoneme is characterized in terms of the identity of its neighboring phonemes. The neighbors influence only the state observation density of a fixed Hidden Markov Model. Other sources of variation are captured implicitly by using Gaussian mixture models for the state observations. Consequently, these models can be very broad, particularly for casual spontaneous speech. In this thesis, we explore conditioning of phonemes on higher level linguistic structure, specifically syllable- and word-level structure to learn models for phonemes that are more specific to the context, reporting experimental results on a large vocabulary (35k words) conversational speech task (Switchboard).

In particular, this thesis makes three main contributions related to wide context conditioning. First, we demonstrate that syllable- and word-level structure can be incorporated into current acoustic models to improve recognition accuracy over triphones. For a fixed number of parameters, these models are computationally more efficient than pentaphones, both in training and in testing. In addition, use of syllable and word features leads to a small but significant improvement in performance. The wide-contexts used in our acoustic model can implicitly capture re-syllabification effects to a certain extent. However, we find that explicitly modeling re-syllabification does not improve recognition further, because there are only a small number of phones that exhibit acoustic difference after re-syllabification.

The second contribution addresses the difficulties that arise when a large number of additional conditioning features are used. As the number of conditioning features increases, the training cost can increase exponentially. Moreover, a large fraction of the training labels tends to have too few examples to have reliable statistics associated with them, and this could potentially cause decision trees to learn bad clusters. A new method has been developed for clustering with multiple stages, where each stage clusters a different subset of features, and also has a choice of using the partitions learned in the previous stages. Apart from reducing the risk of unreliable statistics, it is designed to ameliorate data fragmentation problem and is computationally less expensive. This method was successfully demonstrated with pentaphones, resulting in equivalent performance at a lower cost.

Finally, a new algorithm is described to design context-specific HMMs. The idea is to model reduction of a phone for certain contexts, and to learn a more constrained topology. Using contextual information, the algorithm clusters HMM paths where each path has a different number of states. An HMM distance measure has been formulated to prune out the paths which are similar. During decoding, the paths are allocated dynamically for each sub-word unit according to their context. We investigated this algorithm to model phone topologies, finding improved characterization of speech given known word sequences but no significant improvement in word error rate.

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The full thesis in Postscript.

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