On the interaction between true source, training, and testing language models

Abstract

An interaction has been found between the true source language model, training language model, and the testing language model. This interaction has implications for vocabulary independent modeling, testing methodologies, discriminative training, and the adequacy of our current databases for continuous speech recognition (CSR) development. The current DARPA databases suffer from the described difficulties which suggests that new CSR databases are needed if we are to further advance the state-of-the-art. The Interaction During Training When a category model (e.g. a context-free (CF) model such as a monophone) is used to a model a set of subcategories (e.g. context-dependent (CD) models such as triphones), the category model becomes the subcategory prior-probability weighted average of the subcategory models: Meat E PsubeatMsubcat where M denotes a model. (The mathematics used here are intended to be conceptual rather than rigorous. Thus models will be considered to be averages. In practice, the method for deriving a model from a set of sub-models or observations is highly dependent upon the form of model used.) In a field, such as speech recognition, where models are trained from exemplars, the subcategory model will generally be: N 1 Msttbcat = ~ ~.= Osubeat,i where 08=bcat,i is an observation emitted from the subcategory. Mcat combines both the subcategory models and the prior-probability of the subcategories and similarly Msubcat combines the observations and their (sampled) prior-probabilities.

The design for the wall street journal-based CSR corpus

“…Commencing with serious contractor concerns regarding suitable CSR corpora [12] starting in the mid 1980's, the DARPA SLS Coordinating Committee started considering new corpora requirements in early 1990, with the subsequent formation of the CSR Corpus Committee, culminating in the WSJ Corpus design. The CSR Corpus Committee members include J.M.…”

“…It is important to be able to train a language model that is well matched to the (text) source to be used as a control condition to isolate the performance of the acoustic modeling from the language modeling [12]. (It is always possible to train a mismatched language model, but its effects cannot be adequately assessed without a control matched language model.)…”

“…Commencing with serious contractor concerns regarding suitable CSR corpora [12] Weintraub (SRI), and 3. Wilpon (ATT).…”