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Influence of highly inflected word forms and acoustic background on the robustness of automatic speech recognition for human–computer interactionAndrej Žgank, 2022, original scientific article
Abstract: Automatic speech recognition is essential for establishing natural communication with
a human–computer interface. Speech recognition accuracy strongly depends on the complexity of
language. Highly inflected word forms are a type of unit present in some languages. The acoustic
background presents an additional important degradation factor influencing speech recognition
accuracy. While the acoustic background has been studied extensively, the highly inflected word
forms and their combined influence still present a major research challenge. Thus, a novel type of
analysis is proposed, where a dedicated speech database comprised solely of highly inflected word
forms is constructed and used for tests. Dedicated test sets with various acoustic backgrounds were
generated and evaluated with the Slovenian UMB BN speech recognition system. The baseline word
accuracy of 93.88% and 98.53% was reduced to as low as 23.58% and 15.14% for the various acoustic
backgrounds. The analysis shows that the word accuracy degradation depends on and changes
with the acoustic background type and level. The highly inflected word forms’ test sets without
background decreased word accuracy from 93.3% to only 63.3% in the worst case. The impact of
highly inflected word forms on speech recognition accuracy was reduced with the increased levels of
acoustic background and was, in these cases, similar to the non-highly inflected test sets. The results
indicate that alternative methods in constructing speech databases, particularly for low-resourced
Slovenian language, could be beneficial.
Keywords: human–computer interaction, automatic speech recognition, acoustic modeling, highly inflected word forms, acoustic background
Published in DKUM: 28.03.2025; Views: 0; Downloads: 2
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