Speech retrieval from unsegmented finnish audio using statistical morpheme-like units for segmentation, recognition, and retrieval

Abstract

This article examines the use of statistically discovered morpheme-like units for Spoken Document Retrieval (SDR). The morpheme-like units ( morphs ) are used both for language modeling in speech recognition and as index terms. Traditional word-based methods suffer from out-of-vocabulary words. If a word is not in the recognizer vocabulary, any occurrence of the word in speech will be missing from the transcripts. The problem is especially severe for languages with a high number of distinct word forms such as Finnish. With the morph language model, even previously unseen words can be recognized by identifying its component morphs. Similarly in information retrieval queries, complex word forms, even unseen ones, can be matched to data after segmenting them to morphs. Retrieval performance can be further improved by expanding the transcripts with alternative recognition results from confusion networks . In this article, a novel retrieval evaluation corpus consisting of unsegmented Finnish radio programs, 25 queries and corresponding human relevance assessments was constructed. Previous results on using morphs and confusion networks for Finnish SDR are confirmed and extended to the unsegmented case. As previously, using morphs or base forms as index terms yields about equal performance but combination methods, including a new one, are found to work better than either alone. Using alternative morph segmentations of the query words is found to further improve the results. Lexical similarity-based story segmentation was applied and performance using morphs, base forms, and their combinations was compared for the first time.

Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields

“…This type of morphological analysis can be useful for alleviating language model sparsity inherent to morphologically rich languages (Hirsimäki et al, 2006;Turunen and Kurimo, 2011;Luong et al, 2013). Particularly, we focus on a low-resource learning setting, in which only a small amount of annotated word forms are available for model training, while unannotated word forms are available in abundance.…”

A Comparative Study of Minimally Supervised Morphological Segmentation

“…In particular, model hyperparameters can be selected to optimize segmentation performance, rather than some generative objective, such as likelihood. Special cases of hyperparameter selection include the weighted objective function (Kohonen, Virpioja, and Lagus 2010), data selection (Virpioja, Kohonen, and Lagus 2011;Sirts and Goldwater 2013), and grammar template selection (Sirts and Goldwater 2013). As for the weighted objective function and grammar template selection, the weights and templates are optimized to maximize segmentation accuracy.…”

“…As for the weighted objective function and grammar template selection, the weights and templates are optimized to maximize segmentation accuracy. Meanwhile, data selection is based on the observation that omitting some of the training data can improve segmentation accuracy (Virpioja, Kohonen, and Lagus 2011;Sirts and Goldwater 2013).…”

“…A low-cost alternative, therefore, could be to learn morphological segmentation from unannotated word lists and a handful of annotated examples. Importantly, segmentation analysis has been found useful in a range of applications, such as speech recognition (Hirsimäki et al 2006;Narasimhan et al 2014), information retrieval (Turunen and Kurimo 2011), machine translation (de Gispert et al 2009;Green and DeNero 2012), and word representation learning (Luong, Socher, and Manning 2013;Qiu et al 2014).…”

“…Although this is a major simplification of the diverse morphological phenomena present in languages, this type of analysis has nevertheless been of substantial interest to computational linguistics, beginning with the pioneering work on morphological learning by Harris (1955). As for automatic language processing, such segmentations have been found useful in a wide range of applications, including speech recognition (Hirsimäki et al 2006;Narasimhan et al 2014), information retrieval (Turunen and Kurimo 2011), machine translation (de Gispert et al 2009;Green and DeNero 2012), and word representation learning (Luong, Socher, and Manning 2013;Qiu et al 2014).…”