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Discriminative model combination is a new approach in the field of automatic speech recognition, which aims at an optimal integration of all given (acoustic and language) models into one log-linear posterior probability distribution. As opposed to the maximum entropy approach, the coefficients of the log-linear combination are optimized on training samples(More)
We describe procedures and experimental results using speech from diverse source languages to build an ASR system for a single target language. This work is intended to improve ASR in languages for which large amounts of training data are not available. We have developed both knowledge based and automatic methods to map phonetic units from the source(More)
In this paper we present some experiments that have been performed while developing language models for the PHILIPS Broadcast News system. Three main issues will be discussed: construction of phrases, adaptation of remote corpora to this task, and the combination of the diierent models. Also, per-plexities on the 1997 evaluation data are reported.
Automatic speech recognition of real-live broadcast news (BN) data (Hub-4) has become a challenging research topic in recent years. This paper summarizes our key efforts to build a large vocabulary continuous speech recognition system for the heterogenous BN task without inducing undesired complexity and computational resources. These key efforts included:(More)
This paper contains a description of the PhilipssRWTH 1998 HUB4 system which has been build in a joint eeort of Philips Research Laboratories Aachen and Aachen Uni-versityofTechnology. We will focus our discussion on recent improvements compared to the original 1997 HUB4 system and evaluate them on the HUB4'97 evaluation data. The paper will deal with 1. a(More)
mRNA-seq is a paradigm-shifting technology because of its superior sensitivity and dynamic range and its potential to capture transcriptomes in an agnostic fashion, i.e., independently of existing genome annotations. Implementation of the agnostic approach, however, has not yet been fully achieved. In particular, agnostic mapping of pre-mRNA splice sites(More)
The performance of the Philips system for large vocabulary continuous speech recognition has been improved signiicantly by crossword N-phone modelling, enhanced clustering of HMM-states during training, consistent handling of untrained HMM-states during decoding and a new eecient crossword N-phone M-gram decoding strategy. We report word error rate(More)
The combination of Maximum Likelihood Linear Regression (MLLR) with Maximum a p osteriori (MAP) adaptation has been investigated for both the enrollment of a new speaker as well as for the asymptotic recognition rate after several hours of dictation. We show that a least mean square approach to MLLR is quite eective in conjunction with phonetically derived(More)