Morph-based spoken document retrieval uses morpheme-like subword units for both language modeling and as index terms. Problems of out-of-vocabulary (OOV) words are avoided as the morph recognizer can recognize any word in speech as a sequence of subwords. The effect of previously unseen query words (i.e. words that are not in the language model training text) is analyzed for Finnish spoken document retrieval. The performance of the morph-based system is compared to a wordbased approach. Language models with artificially high OOV query word rates are built and the results show that morphbased retrieval suffers significantly less from the OOV query words than word-based. Extracting alternative recognition candidates from confusion networks further improves the results, especially for morph-based retrieval.

In this paper, we investigate methods for improving the performance of morph-based spoken document retrieval in Finnish by extracting relevant index terms from confusion networks. Our approach uses morpheme-like subword units ("morphs") for recognition and indexing. This alleviates the problem of out-of-vocabulary words, especially with inflectional languages like Finnish. Confusion networks offer a convenient representation of alternative recognition candidates by aligning mutually exclusive terms and by giving the posterior probability of each term. The rank of the competing terms and their posterior probability is used to estimate term frequency for indexing. Comparing against 1-best recognizer transcripts, we show that retrieval effectiveness is significantly improved. Finally, the effect of pruning in recognition is analyzed, showing that when recognition speed is increased, the reduction in retrieval performance due to the increase in the 1-best error rate can be compensated by using confusion networks.

Previously, phone-based and word-based approaches have been used for spoken document retrieval.The former suffers from high error rates and the latter from limited vocabulary of the recognizer.Our method relies on unlimited vocabulary continuous speech recognizer that uses morpheme-like units discovered in an unsupervised manner.The morpheme-like units, or "morphs" for short, have been successfully used also as index terms.One problem using morphs as index terms is that the segmentation does not always separate the same stem for different inflected forms of the same word.This resembles the problem of synonyms.In this paper, we apply latent semantic indexing to morph based retrieval.The idea is to project morphs that correspond to the same word, as well as other semantically related terms, to the same dimension.The results show clear improvements in Finnish spoken document retrieval performance.

An important difference between the retrieval of spoken and written documents is that the indexing of the speech data is usually based on automatic speech transcripts that contain recognition errors. However, there are several ways of reducing the effect of incorrect index terms in the retrieval. This paper presents retrieval experiments with unlimited vocabulary speech recognizer that utilizes a lexicon of unsupervised morpheme-like units. Based on this recognizer, three different methods are evaluated for error recovery. First, the recognized words are expanded by adding the recognized morphemes, too. Second, the words are expanded by adding the best rival morpheme candidates that were pruned away by the recognizer. Third, the queries are expanded by the potentially relevant terms found from text documents, which were retrieved from parallel text corpora by the original queries. The best results are obtained by that latter method which significantly improves the precision compared to the original queries and brings the spoken document retrieval precision to the same level as the corresponding text document retrieval.

This paper presents a baseline spoken document retrieval system in Finnish. Due to its agglutinative structure, Finnish speech can not be adequately transcribed using the standard large vocabulary continuous speech recognition approaches. The definition of a sufficient lexicon and the training of the statistical language models are difficult, because the words appear transformed by many inflections and compounds. In this work we apply a recently developed unlimited vocabulary speech recognition system that allows the use of n-gram language models based on morpheme-like subword units discovered in an unsupervised manner. In addition to word-based indexing, we also propose an indexing based on the subword units provided directly by our speech recognizer. In an initial evaluation of newsreading in Finnish, we obtained a fairly low recognition error rate and average document retrieval precisions close to that from human reference transcripts.