On the influence of hyperarticulated speech on recognition performance

Soltau, Hagen; Waibel, Alex

doi:10.21437/icslp.1998-237

Cited by 11 publications

References 5 publications

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Automatic Detection of Hyperarticulated Speech

Ribeiro

Batista

Trancoso

et al. 2016

Advances in Speech and Language Technologies for Iberian Languages

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Abstract. Hyperarticulation is a speech adaptation that consists of adopting a clearer form of speech in an attempt to improve recognition levels. However, it has the opposite effect when talking to ASR systems, as they are not trained with such kind of speech. We present approaches for automatic detection of hyperarticulation, which can be used to improve the performance of spoken dialog systems. We performed experiments on Let's Go data, using multiple feature sets and two classification approaches. Many relevant features are speaker dependent. Thus, we used the first turn in each dialog as the reference for the speaker, since it is typically not hyperarticulated. Our best results were above 80 % accuracy, which represents an improvement of at least 11.6 % points over previously obtained results on similar data. We also assessed the classifiers' performance in scenarios where hyperarticulation is rare, achieving around 98 % accuracy using different confidence thresholds.

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Automatic Detection of Hyperarticulated Speech

Ribeiro

Batista

Trancoso

et al. 2016

Advances in Speech and Language Technologies for Iberian Languages

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Prosodic and other cues to speech recognition failures

Hirschberg

Litman

Swerts

2004

Speech Communication

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In spoken dialogue systems, it is important for the system to know how likely a speech recognition hypothesis is to be correct, so it can reject misrecognized user turns, or, in cases where many errors have occurred, change its interaction strategy or switch the caller to a human attendant. We have identified prosodic features which predict more accurately when a recognition hypothesis contains errors than the acoustic confidence scores traditionally used in automatic speech recognition in spoken dialogue systems. We describe statistical comparisons of features of correctly and incorrectly recognized turns in the TOOT train information corpus and the W99 conference registration corpus, which reveal significant prosodic differences between the two sets of turns. We then present machine learning results showing that the use of prosodic features, alone and in combination with other automatically available features, can predict more accurately whether or not a user turn was correctly recognized, when compared to the use of acoustic confidence scores alone.

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The dual of denial: Two uses of disconfirmations in dialogue and their prosodic correlates

Krahmer

Swerts

Theune

et al. 2002

Speech Communication

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show abstract

On the influence of hyperarticulated speech on recognition performance

Cited by 11 publications

References 5 publications

Automatic Detection of Hyperarticulated Speech

Automatic Detection of Hyperarticulated Speech

Prosodic and other cues to speech recognition failures

The dual of denial: Two uses of disconfirmations in dialogue and their prosodic correlates

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