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Moreover, we could reach 75.4% and 71.6% of ERR relative to the baseline by using real utterances by five speakers and one speaker, respectively.
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However, to the best of our knowledge, the impact of state-of-the-art speech enhancement techniques has not been analyzed for text-independent automatic speaker verification (ASV) systems using real-world utterances.
By using the same number of utterances (40 pairs of utterances) in the experiments using real reverberant data, we could reach 75.4% and 71.6% of ERR relative to the baseline by using '5s.40u' (5 speakers) and '1s.40u' (1 speaker).
Table 15 Environment-specific NN training datasets for the experiments using real reverberant data Dataset name Speaker number Utterances num.
Table 14 Multi-condition (combined) NN training datasets for the experiments using real reverberant data Dataset name Speaker number Utterances num.
Also in matched condition cases, the experimental result using real data shows that by using '6 NNs' configuration and only one pair of utterances for the NN training data, we could reach 34.8% of ERR relative to the use of CMN.
For the experiments using real dataset, it is necessary to use CMN because we need to remove the noise and reverberation in the close-talking utterances used to train the speaker models.
The proposed cost function is evaluated by using speech utterances and unconstrained face images for age and gender classification task.
From the evaluation using ASR, by using 40 pairs of utterances as the NN training data, we could reach 78.4% of ERR relative to the baseline by using simulated utterances by five speakers.
This can be solved by using incomplete utterances, jokes, quoting 'third parties' and prompting for collaborative utterances.
This is best done by verbal communication, for example by using single words referring to the relevant objects or by using longer utterances.
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