Sentence examples for speech variability from inspiring English sources

Studies have shown that articulatory information helps model speech variability and, consequently, improves speech recognition performance.

In this paper, together with a detailed description of the database, some experimental results including different speech variability factors are also presented.

This chapter provides an overview of an automatic speech recognition system and describes sources of speech variability that cause mismatch between training and testing.

A range of objective, computer-based measures of speech tapping speech production (silence, number and length of pauses, number and length of utterances), speech variability (global and local intonation and emphasis) and speech content (word fillers, idea density) were employed.

Human machine interaction for all of these areas requires the existence of speech analysis, speech recognition, and speech verification algorithms that are robust with respect to the sources of speech variability that are characteristic of this population of speakers.

An invariant representation of speech, in both biological and artificial systems, is crucial for improving the robustness of the acoustic to phonetic mapping, decreasing the sample complexity (i.e., the number of labeled examples) and enhancing the generalization performance of learning in the presence of distribution mismatch due to speech variability.

This was somewhat surprising given the greater speech production variability and poorer production contrast between /s/ and / ∫/ production.

There are different ways to address speaker variability for automatic speech recognition.

The findings indicate that paralinguistic aspects of speech, especially pitch variability, are promising measures to gain information about fear processing during the recollection of autobiographical memories.

A combined vowel perception/vowel production study was designed to address the question of how variability in speech production relates to variability in speech perception.

This is consistent with the known fact that children's speech has higher intraspeaker variability than adults' speech leading to larger variance of the acoustic models [13].