In this paper, we present our research on dialog dependent language modeling. In accordance with a speech (or sentence) production model in a discourse we split language modeling into two components; namely, dialog dependent concept modeling and syntactic modeling. The concept model is conditioned on the last question prompted by the dialog system and it is structured using #-grams. The syntactic model , which consists of a collection of stochastic context-free grammars one for each concept, describes word sequences that may be used to express the concepts. The resulting LM is evaluated by rescoring #-best lists. We report significant perplexity improvement with moderate word error rate drop within the context of CU Communicator System; a dialog system for making travel plans by accessing information about flights, hotels and car rentals.

We present context-sensitive dynamic classes – a novel mechanism for integrating contextual information from spoken dialogue into a class n-gram language model. We exploit the dialogue system’s information state to populate dynamic classes, thus percolating contextual constraints to the recognizer’s language model in real time. We describe a technique for training a language model incorporating context-sensitive dynamic classes which considerably reduces word error rate under several conditions. Significantly, our technique does not partition the language model based on potentially artificial dialogue state distinctions; rather, it accommodates both strong and weak expectations via dynamic manipulation of a single model. 1.

We explore several language modeling strategies for increasing the recognition accuracy among large sets of proper nouns in a mapbased multimodal dialogue system which provides restaurant information. In particular, we evaluate several mechanisms for exploiting dialogue context, the two most promising of which involve a semistatic metropolitan-region based large set of proper nouns competing with a smaller, in-focus subset. We show that these techniques decrease word, concept, and proper noun error rates under several training conditions. We also present a technique to generalize sparse training data through derived templates to improve language model robustness. Index Terms — multimodal dialogue system, language modeling, context-sensitive, restaurants, proper nouns 1.

Google offers several speech features on the Android mobile operating system: search by voice, voice input to any text field, and an API for application developers. As a result, our speech recognition service must support a wide range of usage scenarios and speaking styles: relatively short search queries, addresses, business names, dictated SMS and e-mail messages, and a long tail of spoken input to any of the applications users may install. We present a method of on-demand language model interpolation in which contextual information about each utterance determines interpolation weights among a number of n-gram language models. On-demand interpolation results in an 11.2 % relative reduction in WER compared to using a single language model to handle all traffic. Index Terms: language modeling, interpolation, mobile 1.

The aim of this paper is to describe the experiences gained in the field of language modelling during the LE-3 ARISE (Automatic Railway Information Systems for Europe) project. All of the different techniques presented in this paper are related to the field of Spoken Dialogue Systems, and they cope with the issues of limited amount of training material and the exploitation of the constraints available in a dialogue system. The results obtained may be useful for the future development of similar applications. Keywords: language modelling, spoken dialogue system, speech recognition

een wetenschappelijke proeve op het gebied van de Letteren Proefschrift ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de Rector Magnificus