This paper describes the development of the RWTH Mandarin LVCSR system. Different acoustic front-ends together with multiple system cross-adaptation are used in a two stage decoding framework. We describe the system in detail and present systematic recognition results. Especially, we compare a variety of approaches for cross-adapting to multiple systems.During the development we did a comparative study on different methods for integrating tone and phoneme posterior features. Furthermore, we apply lattice based consensus decoding and system combination methods. In these methods, the effect of minimizing character instead of word errors is compared. The final system obtains a character error rate of 17.7% on the GALE 2006 evaluation data.
This paper describes the rapid development of a Polish language speech recognition system. The system development was performed without access to any transcribed acoustic training data. This was achieved through the combined use of cross-language bootstrapping and confidence based unsupervised acoustic model training. A Spanish acoustic model was ported to Polish, through the use of a manually constructed phoneme mapping. This initial model was refined through iterative recognition and retraining of the untranscribed audio data.The system was trained and evaluated on recordings from the European Parliament, and included several state-of-the-art speech recognition techniques in addition to the use of unsupervised model training. Confidence based speaker adaptive training using features space transform adaptation, as well as vocal tract length normalization and maximum likelihood linear regression, was used to refine the acoustic model. Through the combination of the different techniques, good performance was achieved on the domain of parliamentary speeches.
This paper describes the current improvements of the RWTH Mandarin LVCSR system. We introduce vocal tract length normalization for the Gammatone features and present comparable results for Gammatone based feature extraction and classical feature extraction. In order to benefit from the huge amount of data of 1600h available in the GALE project we have trained the acoustic models up to 8M Gaussians. We present detailed character error rates for the different number of Gaussians.Different kinds of systems are developed and a two stage decoding framework is applied, which uses cross-adaptation and a subsequent lattice-based system combination. In addition to various acoustic front-ends, these systems use different kinds of neural network toneme posterior features. We present detailed recognition results of the development cycle and the different acoustic front-ends of the systems. Finally, we compare the ultimate evaluation system to our last years system and can report a 10% relative improvement.
We announce the public availability of the RWTH Aachen University speech recognition toolkit. The toolkit includes state of the art speech recognition technology for acoustic model training and decoding. Speaker adaptation, speaker adaptive training, unsupervised training, a finite state automata library, and an efficient tree search decoder are notable components. Comprehensive documentation, example setups for training and recognition, and a tutorial are provided to support newcomers.
In this work, the RWTH automatic speech recognition systems developed for the third TC-STAR evaluation campaign 2007 are presented. The RWTH systems make systematic use of internal system combination, combining systems with differences in feature extraction, adaptation methods, and training data used. To take advantage of this, novel feature extraction methods were employed; this year saw the introduction of Gammatone features and MLP based phone posterior features. Further improvements were achieved using unsupervised training, and it is notable that these improvements were achieved using a fairly low amount of automatically transcribed data. Also contributing to the improvements over last year was the switch to MPE training, and the introduction of projecting SAT transforms.
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