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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)
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)
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)
The paper contains a description of the Philips/RWTH 1998 HUB4 system which was build in a joint eeort of Philips Research Laboratories Aachen and Aachen University of Technology. We will focus our discussion on recent improvements compared to the original 1997 HUB4 system Beyerlein + 1998] and evaluate them on the HUB4'97 evaluation data. The paper will(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)
We develop an acoustic feature set for the estimation of a per-son's age from a recorded speech signal. The baseline features are Mel-frequency cepstral coefficients (MFCCs) which are extended by various prosodic features, pitch and formant frequencies. From experiments on the University of Florida Vocal Aging Database we can draw different conclusions. On(More)