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Inference for each of these models was carried out using the training set and were evaluated using the evaluation set.
The results presented in this paper have been obtained using the evaluation set of the REVERB challenge [26], which consists of a large corpus of speech corrupted by reverberation and noise.
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Subsequently, the system was trained on both the training and development sets using the optimal settings, and then evaluated on the test set using the evaluation systems provided by the ST organizers.
The ROSA validity and reliability were analyzed using the safety evaluation set as well as the FAS baseline study data, both resulting in the same factor structure of the ROSA and similar psychometric characteristics.
We optimized all model weights and the word insertion penalty again on the development set and used them in the evaluation set.
Table 5 shows the WER for the evaluation set using an acoustic model trained with the baseline training data set.
Table 6 Results for the evaluation set using an acoustic model trained with extended training data Proc.
Table 5 Results for the evaluation set using an acoustic model trained with the baseline multi-condition training data set Proc.
Table 10 Recognition results on the evaluation set using simple MSD models for POS tags (P) and MSD tags (M) First pass Second pass WER Relative model model improvement Bigram M 25.84 +11.56 Bigram M+P 26.01 +10.99 Trigram M 25.86 −0.84 Trigram M+P 25.62 +0.07.
We predict the stages of the images from the evaluation set using the ensemble of different subset of methods, and the results are summarized in Table 2. Apart from the ensemble of all methods, we test three other scenarios: SVM models alone, sparse models alone and SVM models plus sparse models with logistic loss.
We repeated the recognition on the development set in order to obtain optimal parameters for the language model weight and the word insertion penalty and used the optimized values on the evaluation set.
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