Sentence examples for speech recognition rate from inspiring English sources

Acoustic model adaptation can improve the speech recognition rate and additional information in rich transcription can be useful when searching for some certain events or circumstances (male speaker talking over the phone line, etc).

We obtained a 75.0% speech recognition rate with a 66.7% accuracy.

As a result, although the speech recognition rate is problematic, inputting with saying a sentence directly is much faster than handwriting.

For the toneless languages, the quickly changing events in speech can also benefit from this merit, leading to a somewhat improved speech recognition rate.

Using the SS-based dereverberation method, the reduction in the speech recognition rate was smaller than in conventional CMN, especially for impulse responses with a long reverberation time.

In this paper, we also evaluated the SS-based dereverberation method on LVCSR with the experimental results shown in Figure 5. Naturally, the speech recognition rate deteriorated as the reverberation time increased.

TellMe, which Microsoft bought two years ago, is rolling out an upgrade to its call center automation software which should improve its speech recognition rates.

Towards Increasing Speech Recognition Error Rates.

First, we demonstrate that speech recognition error rates for interactive read aloud can be reduced by more than 50% through a combination of advances in both statistical language and acoustic modeling.

Such a system has to deal with speakers that have a very low proficiency of the foreign language, thus speech recognition error rates are very high.

Voice source parameters like jitter and shimmer change with age, but they are shown to have just a minor influence on speech recognition error rates.