Corrective Models for Speech Recognition of Inflected Languages

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 390–398, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Corrective Models for Speech Recognition of Inflected Languages Izhak Shafran and Keith Hall Center for Language and Speech Processing Johns Hopkins University Baltimore, MD 21218 {zakshafran,keith hall}@jhu.edu Abstract This paper presents a corrective model for speech recognition of inflected lan- guages. The model, based on a discrim- inative framework, incorporates word n- grams features as well as factored mor- phological features, providing error reduc- tion over the model based solely on word n-gram features. Experiments on a large vocabulary task, namely the Czech portion of the MALACH corpus, demonstrate per- formance gain of about 1.1–1.5% absolute in word error rate, wherein morphologi- cal features contribute about a third of the improvement. A simple feature selection mechanism based on χ2 statistics is shown to be effective in reducing the number of features by about 70% without any loss in performance, making it feasible to explore yet larger feature spaces. 1 Introduction N-gram models have long been the stronghold of statistical language modeling approaches. Within the n-gram paradigm, straightforward approaches for increasing accuracy include using larger train- ing sets and augmenting the contextual informa- tion within the n-gram window. Incorporating syntactic features into the context has been at the forefront of recent research (Collins et al., 2005; Rosenfeld et al., 2001; Chelba and Jelinek, 2000; Hall and Johnson, 2004). However, much of the previous work has focused on English language syntax. This paper addresses syntax as captured by the inflectional morphology of highly inflected language. High inflection in a language is generally cor- related with some level of word-order flexibil- ity. Morphological features either directly identify or help disambiguate the syntactic participants of a sentence. Inflectional morphology works as a proxy for structured syntax in a language. Model- ing morphological features in these languages not only provides an additional source of information but can also alleviate data sparsity problems. Czech speech recognition needs to deal with two sources of errors which are absent in En- glish, namely, the inflectional morphology and the differences in the formal (written) and colloquial (spoken) forms. Table 1 presents an example out- put of our speech recognizer on an utterance from a Holocaust survivor, who is recounting General Romel’s desert campaign during the Second World War. In this example, the feminine past-tense form of the Czech verb for to be is chosen mis- takenly, which is followed by a sequence of in- correct words chosen primarily to maintain agree- ment with the feminine form of the verb. This is an example of what we refer to as the morpho- logical grouping effect. When the acoustic model prefers a word with an incorrect inflection, the lan- guage model effectively propagates the error to later words. A language model based on word- forms prefers sequences observed in the training data, which will implicitly force an agreement with the inflections of preceding words, making it difficult …

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