An Unsupervised Quantitative Measure for Word Prominence in Spontaneous Speech

Wang, Dagen; Narayanan, S. R.

doi:10.1109/icassp.2005.1415129

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Speech rate is often measured in words per minute, syllables per second (SPS) or phonemes per second. Of these, SPS was used in this study as it has a fairly uniform distribution and is robust against variations in speech (Wang & Narayanan, 2005). Calculation of syllables is based on the structural syllabification of the text, which is realized by using the Natural Language Toolkit (Bird, Klein & Loper, 2009).…”

Section: Selection Criteria For Pscmentioning

confidence: 99%

Partial and synchronized captioning: A new tool to assist learners in developing second language listening skill

Mirzaei

Meshgi

Akita

et al. 2017

ReCALL

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This paper introduces a novel captioning method, partial and synchronized captioning (PSC), as a tool for developing second language (L2) listening skills. Unlike conventional full captioning, which provides the full text and allows comprehension of the material merely by reading, PSC promotes listening to the speech by presenting a selected subset of words, where each word is synched to its corresponding speech signal. In this method, word-level synchronization is realized by an automatic speech recognition (ASR) system, dedicated to the desired corpora. This feature allows the learners to become familiar with the correspondences between words and their utterances. Partialization is done by automatically selecting words or phrases likely to hinder listening comprehension. In this work we presume that the incidence of infrequent or specific words and fast delivery of speech are major barriers to listening comprehension. The word selection criteria are thus based on three factors: speech rate, word frequency and specificity. The thresholds for these features are adjusted to the proficiency level of the learners. The selected words are presented to aid listening comprehension while the remaining words are masked in order to keep learners listening to the audio. PSC was evaluated against no-captioning and full-captioning conditions using TED videos. The results indicate that PSC leads to the same level of comprehension as the full-captioning method while presenting less than 30% of the transcript. Furthermore, compared with the other methods, PSC can serve as an effective medium for decreasing dependence on captions and preparing learners to listen without any assistance.

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Section: Selection Criteria For Pscmentioning

confidence: 99%

Partial and synchronized captioning: A new tool to assist learners in developing second language listening skill

Mirzaei

Meshgi

Akita

et al. 2017

ReCALL

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“…Speech rate also correlates with prosodic prominence. Detection and normalization of rate of speech has been found to be necessary in measuring such attributes [8], [21]. Global speech rate also works as a normalization factor for many prosody-based classifiers.…”

Section: A Significancementioning

confidence: 99%

Robust Speech Rate Estimation for Spontaneous Speech

Wang

Narayanan

2007

IEEE Trans. Audio Speech Lang. Process.

101

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In this paper, we propose a direct method for speech rate estimation from acoustic features without requiring any automatic speech transcription. We compare various spectral and temporal signal analysis and smoothing strategies to better characterize the underlying syllable structure to derive speech rate. The proposed algorithm extends the methods of spectral subband correlation by including temporal correlation and the use of prominent spectral subbands for improving the signal correlation essential for syllable detection. Furthermore, to address some of the practical robustness issues in previously proposed methods, we introduce some novel components into the algorithm such as the use of pitch confidence for filtering spurious syllable envelope peaks, magnifying window for tackling neighboring syllable smearing, and relative peak measure thresholds for pseudo peak rejection. We also describe an automated approach for learning algorithm parameters from data, and find the optimal settings through Monte Carlo simulations and parameter sensitivity analysis. Final experimental evaluations are conducted based on a portion of the Switchboard corpus for which manual phonetic segmentation information, and published results for direct comparison are available. The results show a correlation coefficient of 0.745 with respect to the ground truth based on manual segmentation. This result is about a 17% improvement compared to the current best single estimator and a 11% improvement over the multiestimator evaluated on the same Switchboard database.

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“…PPS has its own limitations as the relation between phonemes and speech rate is neither linear nor simple (Siegler, 1995). SPS, on the other hand, has fairly uniform distribution over speech rate and is more robust against variations in speech (Wang and Narayanan, 2005), thereby used as a unit of measurement in PSC. To estimate the speech rate of each word in SPS, the system calculates the duration of the word obtained from the force-alignment procedure and uses Knuth-Liang hyphenation algorithm to syllabify each word (Liang, 1983).…”

Section: Baseline Psc: Feature Calculationmentioning

confidence: 99%