Sentence examples for word recognition error from inspiring English sources

An approach to evaluate a language model is word recognition error rate [60].

For RWCP database, the SS-based dereverberation method achieved a relative word recognition error reduction rate of 19.2% relative to CMN with delay-and-sum beamforming.

Personal digital assistants like Siri and Cortana have grown increasingly more sophisticated, with word recognition errors dropping consistently year after year, and accordingly, consumers are becoming more reliant on voice search for their online search needs.

Within this context, it is therefore important to observe that word recognition errors are highly influenced by nonauditory factors.

Another important paper by M. Coene is discussing the spoken word recognition errors in speech audiometry on normal subjects with complete speech intelligibility.

The input to the system is a sign from RWTH-BOSTON-50 database and the output is the corresponding word with a recognition error rate of 10.90%.

In this paper, the central question that is raised is at the heart of the ongoing debate with respect to the interaction versus autonomous processing models in speech perception research: How do auditory cues, phonological neighborhood, and word frequency contribute to phoneme identification errors in word recognition tasks?

That is, what can we learn about the structure of semantic representations from the distribution of semantic errors in various word recognition and production tasks?

Many errors occur only in the grammatical sense, when a recognition error results in a word with the same lemma but with a grammatical error, e.g., a false case or number.

This prediction error signal explains the efficiency of human word recognition and simulates neural responses in auditory regions.

Visual word recognition latencies are longer for LF words.