Speech Rate Estimation via Temporal Correlation and Selected Sub-Band Correlation

Narayanan, S. R.; Wang, Dagen

doi:10.1109/icassp.2005.1415138

Cited by 15 publications

(15 citation statements)

References 4 publications

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“…Such advantages are demonstrated by improved correlation coefficients and reduced mean error and standard deviation in the estimates with respect to the reference values. We have also systematized the heuristic parameter setting methodology originally used in [18]. The Monte Carlo method and dynamic parameter perturbation schemes provide ways for parameter tuning that guarantee finding the local maximum and approximating the global maximum.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Robust Speech Rate Estimation for Spontaneous Speech

Wang

Narayanan

2007

IEEE Trans. Audio Speech Lang. Process.

101

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“…As a result vowels, due to their well-defined formant structure, could be boosted in the correlation envelope while the consonants and noises are not. In a preliminary study, we found that such an approach could help boost the center syllable magnitude and be more noise robust [6].…”

Section: E Acoustic Correlates Of Prominencementioning

confidence: 96%

“…Specifically, in this paper we adapt and expand on the algorithm proposed for speech rate estimation in [17]. We have shown such an approach could provide state of the art speech rate estimation performance [6]. As a side product, the method also provides syllable nucleus information.…”

Section: E Acoustic Correlates Of Prominencementioning

confidence: 99%

An Acoustic Measure for Word Prominence in Spontaneous Speech

Wang

Narayanan

2007

IEEE Trans. Audio Speech Lang. Process.

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An algorithm for automatic speech prominence detection is reported in this paper. We describe a comparative analysis on various acoustic features for word prominence detection and report results using a spoken dialog corpus with manually assigned prominence labels. The focus is on features such as spectral intensity and speech rate that are directly extracted from speech based on a correlation-based approach without requiring explicit linguistic or phonetic knowledge. Additionally, various pitch-based measures are studied with respect to their discriminating ability for prominence detection. A parametric scheme for modeling pitch plateau is proposed and this feature alone is found to outperform the traditional local pitch statistics. Two sets of experiments are used to explore the usefulness of the acoustic score generated using these features. The first set focuses on a more traditional way of word prominence detection based on a manually-tagged corpus. A 76.8% classification accuracy was achieved on a corpus of role-playing spoken dialogs. Due to difficulties in manually tagging speech prominence into discrete levels (categories), the second set of experiments focuses on evaluating the score indirectly. Specifically, through experiments on the Switchboard corpus, it is shown that the proposed acoustic score can discriminate between content word and function words in a statistically significant way. The relation between speech prominence and content/function words is also explored. Since prominent words tend to be predominantly content words, and since content words can be automatically marked from text-derived part of speech (POS) information, it is shown that the proposed acoustic score can be indirectly cross-validated through POS information.

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“…Finally, the spectral-temporal correlation of the i-th phone segment is obtained by summing the correlation among the selected subbands over the interval of the segment. Interested readers are referred to [6] for a more detailed information of the procedures.…”

Section: Spectral-temporal Correlationmentioning

confidence: 99%

“…Then subband selection is performed by following the procedures proposed in [6]. The purpose of this subband selection module is to focus on the sonorant frequency bands.…”

Section: Spectral-temporal Correlationmentioning

confidence: 99%

Study of Prominence Detection Based on Various Phone-Specific Features

Kim

Han

Kim

2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn this letter, we present useful features accounting for pronunciation prominence and propose a classification technique for prominence detection. A set of phone-specific features are extracted based on a forced alignment of the test pronunciation provided by a speech recognition system. These features are then applied to the traditional classifiers such as the support vector machine (SVM), artificial neural network (ANN) and adaptive boosting (Adaboost) for detecting the place of prominence.

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Speech Rate Estimation via Temporal Correlation and Selected Sub-Band Correlation

Cited by 15 publications

References 4 publications

Robust Speech Rate Estimation for Spontaneous Speech

Robust Speech Rate Estimation for Spontaneous Speech

An Acoustic Measure for Word Prominence in Spontaneous Speech

Study of Prominence Detection Based on Various Phone-Specific Features

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