Sentence examples for model adaptation techniques from inspiring English sources

Due to these advantages, numerous environmental model adaptation techniques have been proposed for robust speech recognition until recently.

Since whispered speech is characterized by noise and shift in formants to higher frequencies, newer model adaptation techniques can play a major role in this context.

The model adaptation techniques fail to perform in constantly changing environments where little or no adaptation data is available and hybrid methods attempt to preprocess speech signals and depend on reliability of estimations of those segments.

The feature compensation and model adaptation techniques employed in this experiment are basically conducted in the utterance-by-utterance basis to estimate the required statistics such as mean and variance.

In this article, we review and examine for several uncertainty decoding [1 5], missing feature [6 9], and model adaptation techniques [10 19] how their compensation rules can be formulated as an approximated or modified Bayesian decoding rule.

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}.

Some of them are based on a model adaptation technique.

In the HEQ-MA with mean and variance adaptation approach, we used an SNR-dependent covariance model adaptation technique.

Therefore, the model adaptation technique should have computational efficiency as well as noise robustness in its application to real-time speech recognition.

Another representative model adaptation technique is the vector Taylor series (VTS) approach [8], which linearly approximates noisy speech models from both clean speech and noise models by using the Taylor series expansion.

Additionally, the performance of the standard model adaptation technique based on the MLLR method was also evaluated by using the HTK toolkit [22] and compared with those of the above mentioned techniques.