. Initially introduced in the late 1960s and early 1970s, dynamic programming algorithms have become increasingly popular in automatic speech recognition. There are two reasons why this has occurred: First, the dynamic programming strategy can be combined with avery e#cient and practical pruning strategy so that very large search spaces can be handled. Second, the dynamic programming strategy has turned out to be extremely #exible in adapting to new requirements. Examples of such requirements are the lexical tree organization of the pronunciation lexicon and the generation of a word graph instead of the single best sentence. In this paper, we attempt to systematically review the use of dynamic programming search strategies for small#vocabulary and large#vocabulary continuous speech recognition. The following methods are described in detail: search using a linear lexicon, search using a lexical tree, language-model look-ahead and word graph generation. 1 Introduction Search strategie...

Mismatch is known to degrade the performance of speech recognition systems. In real life applications mismatch is usually nonstationary, and a general way to compensate for slowly time varying mismatch is by using sequential algorithms with forgetting. The choice of forgetting factor is usually performed empirically on some development data, and no optimality criterion is used. In this paper we introduce a framework for obtaining optimal forgetting factor. The proposed method is applied in conjunction with a sequential noise estimation algorithm, but can be extended to sequential bias or affine transformation estimation. Speech recognition experiments conducted first under a controlled scenario on the 5K Wall Street Journal task corrupted by different noise types, then under a real-life scenario on speech recorded in a noisy car environment validate the proposed method.

In this paper we discuss the role of language modeling in a novel natural language dialogue system designed to automatically route incoming customer calls. We arrive at two significant conclusions: First, standard word error rate measures do not reflect application specific requirements; highly reliable content extraction is possible with relatively high word error rates. Secondly blending human-human data with human-machine data did not improve the performance in language modeling.

We describe the integration of across-word models in the RWTH large vocabulary continuous speech recognition system, where our main focus is on the realization of the acoustic recognition process. This paper presents a study of two search methods based on the priniciple of dynamic programming. For both methods we discuss the implementation details and give experimental results on the Verbmobil and on the Wall Street Journal data. In addition, we introduce a score interpolation of within-word and across-word models for both search methods. In combination with across-word models this interpolation technique gives an improvement of the recognition accuracy by 14% relative to our standard system. 1. INTRODUCTION This paper describes the integration of across-word modelling into the RWTH large vocabulary continuous speech recognition system [5]. In particular, we consider two search methods, namely the n-best and one-pass approach, for handling across-word models. Both methods are based o...

Hackers" have written malicious programs to exploit online services intended for human users. As a result, service providers need a method to tell whether a web site is being accessed by a human or a machine. We expect a parallel scenario as spoken language interfaces become common.