Latent phonetic analysis: Use of singular value decomposition to determine features for CRF phone recognition

We exploit an analogy between document retrieval and phone recognition, and adapt the method of latent semantic analysis for the latter task. By mapping into a space of reduced dimensionality, we hope to uncover previously unexploited relationships between posterior estimates of phonetic events and the parts of phones represented by HMM states. We find that features defined over the reduced space complement those previously known, such as, for example, phonological features. We are able to effectively combine all of these features in a phone recognition task by using the constraint-based framework of conditional random fields (CRFs), which allows the use of large and highly redundant feature spaces.