Sentence examples for model adaptation approach from inspiring English sources

Basically, the model adaptation approach needs to adapt the entire model parameters employed in the speech recognizer.

Our model adaptation approach is aimed to provide the speech recognizer with robustness against acoustic noise.

In this paper, we propose a new efficient model adaptation approach based on the histogram equalization (HEQ) technique [10].

Due to its computational efficiency as well as noise robustness, the proposed technique can be another model adaptation approach to robust speech recognition under noisy environments.

According to the experimental results, the proposed model adaptation approach provides substantial effectiveness in reducing the mismatch between trained acoustic models and test environments.

However, due to the potential superiority of the model adaptation approach, it is expected that the use of HEQ in model adaptation can provide more robustness in noisy environments.

One is through the model adaptation approaches, which adapt the clean model towards the distorted speech features.

Model adaptation approaches modify the acoustic model parameters' match with the observed speech features[4, 7].

As mentioned before, uncertainty decoding techniques allow for a time-varying pdf p(b n ), while model adaptation approaches, such as in Subsections 6.1, 6.2, and 6.6.1, mostly set p(b n ) to be constant over time.

The abstract perspective taken in this paper reveals a fundamental difference between model adaptation approaches on the one hand and missing feature and uncertainty decoding approaches on the other hand: Model adaptation techniques usually assume b n to have constant statistics over time [4, 27], i.e., p(mathbf{b}_{n}) = text{const.}, text{for}~ n in {1,ldots,N}.

A lot of robust speech recognition approaches have been proposed to reduce the acoustic mismatch in the past few decades and most of them can be categorized into feature compensation, model adaptation, and uncertainty-based approaches [2].