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While a range of measures based on speech production, language, and perception are possible (Manun et al., 2020) for the prediction and estimation of speech intelligibility, what constitutes second language (L2) intelligibility remains under-defined. Prosodic and temporal features (i.e., stress, speech rate, rhythm, and pause placement) have been shown to impact listener perception (Kang et al., 2020). Still, their relationship with highly intelligible speech is yet unclear. This study aimed to characterize L2 speech intelligibility. Acoustic analyses, including PRAAT and Python scripts, were conducted on 405 speech samples (30 s) from 102 L2 English speakers with a wide variety of backgrounds, proficiency levels, and intelligibility levels. The results indicate that highly intelligible speakers of English employ between 2 and 4 syllables per second and that higher or lower speeds are less intelligible. Silent pauses between 0.3 and 0.8 s were associated with the highest levels of intelligibility. Rhythm, measured by Δ syllable length of all content syllables, was marginally associated with intelligibility. Finally, lexical stress accuracy did not interfere substantially with intelligibility until less than 70% of the polysyllabic words were incorrect. These findings inform the fields of first and second language research as well as language education and pathology.
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Intelligibility as a measure of speech perception: Current approaches, challenges, and recommendations
Intelligibility measures, which assess the number of words or phonemes a listener correctly transcribes or repeats, are commonly used metrics for speech perception research. While these measures have many benefits for researchers, they also come with a number of limitations. By pointing out the strengths and limitations of this approach, including how it fails to capture aspects of perception such as listening effort, this article argues that the role of intelligibility measures must be reconsidered in fields such as linguistics, communication disorders, and psychology. Recommendations for future work in this area are presented.
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- PAR ID:
- 10415768
- Date Published:
- Journal Name:
- The Journal of the Acoustical Society of America
- Volume:
- 153
- Issue:
- 1
- ISSN:
- 0001-4966
- Page Range / eLocation ID:
- 68 to 76
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Predicting the intelligibility of noisy recordings is difficult and most current algorithms treat all speech energy as equally important to intelligibility. Our previous work on human perception used a listening test paradigm and correlational analysis to show that some energy is more important to intelligibility than other energy. In this paper, we propose a system called the Bubble Cooperative Network (BCN), which aims to predict important areas of individual utterances directly from clean speech. Given such a prediction, noise is added to the utterance in unimportant regions and then presented to a recognizer. The BCN is trained with a loss that encourages it to add as much noise as possible while preserving recognition performance, encouraging it to identify important regions precisely and place the noise everywhere else. Empirical evaluation shows that the BCN can obscure 97.7% of the spectrogram with noise while maintaining recognition accuracy for a simple speech recognizer that compares a noisy test utterance with a clean reference utterance. The masks predicted by a single BCN on several utterances show patterns that are similar to analyses derived from human listening tests that analyze each utterance separately, while exhibiting better generalization and less context-dependence than previous approaches.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1121/10.0016806
