Speech-adaptive time-scale modification for computer assisted language-learning

Donnellan, Olivia; Jung, Elmar; Coyle, Eugene

doi:10.1109/icalt.2003.1215049

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…1 Corresponding author 2 time-scaled by applying different scaling factors to different speech segments, depending on the broad phonetic characteristics, without reducing its quality and naturalness (Donnellan et al, 2003). It was concluded in Kuwabara and Nakamura (2000) that voiced frames need to be more affected by time-scaling than mixed frames, and much more than unvoiced frames (Campbell and Isard, 1991).…”

Section: Broad Phonetic Classification Using Discriminative Bayesian mentioning

confidence: 99%

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Broad phonetic classification using discriminative Bayesian networks

Pernkopf

Tuấn

Bilmes

2009

Speech Communication

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Section: Broad Phonetic Classification Using Discriminative Bayesian mentioning

confidence: 99%

“…To maintain the characteristics of plosives or parts of plosives (a closure or release), time-scale modification should not be so applied. Silence frames, moreover, should be treated like voiced frames (Donnellan et al, 2003).…”

Section: Broad Phonetic Classification Using Discriminative Bayesian mentioning

confidence: 99%

Broad phonetic classification using discriminative Bayesian networks

Pernkopf

Tuấn

Bilmes

2009

Speech Communication

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“…Time stretching corresponds to the extension of the signal, but this term is used as a synonym for TSM. Audio time stretching has numerous applications, such as fast browsing of speech recordings [4], music production [5], foreign language and music learning [6], fitting of a piece of music to a prescribed time slot [7], and slowing down the soundtrack for slow-motion video [8]. Additionally, TSM is often used as a processing step in pitch shifting, which aims at changing the frequencies in the signal without changing its duration [2,3,7,9,10].…”

Section: Introductionmentioning

confidence: 99%

Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Damskägg

Välimäki

2017

Applied Sciences

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A novel method for audio time stretching has been developed. In time stretching, the audio signal's duration is expanded, whereas its frequency content remains unchanged. The proposed time stretching method employs the new concept of fuzzy classification of time-frequency points, or bins, in the spectrogram of the signal. Each time-frequency bin is assigned, using a continuous membership function, to three signal classes: tonalness, noisiness, and transientness. The method does not require the signal to be explicitly decomposed into different components, but instead, the computing of phase propagation, which is required for time stretching, is handled differently in each time-frequency point according to the fuzzy membership values. The new method is compared with three previous time-stretching methods by means of a listening test. The test results show that the proposed method yields slightly better sound quality for large stretching factors as compared to a state-of-the-art algorithm, and practically the same quality as a commercial algorithm. The sound quality of all tested methods is dependent on the audio signal type. According to this study, the proposed method performs well on music signals consisting of mixed tonal, noisy, and transient components, such as singing, techno music, and a jazz recording containing vocals. It performs less well on music containing only noisy and transient sounds, such as a drum solo. The proposed method is applicable to the high-quality time stretching of a wide variety of music signals.

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“…Attempts to incorporate nonuniform duration modification are reported in the literature [4], [10], [11].…”

mentioning

confidence: 99%

“…Malcolm Slaney et al have used nonuniform time scaling along with spectral shape and pitch modification for automatically morphing one sound to another sound [10]. Olovia Donnellan et al have proposed a method for speech adaptive TSM, which allows slowing down speech without compromising the quality or naturalness of the slowed speech [11]. In their method, different scaling factors are applied to different types of speech segments.…”

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confidence: 99%

Duration modification using glottal closure instants and vowel onset points

Rao

Yegnanarayana

2009

Speech Communication

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This paper proposes a method for duration (time scale) modification using Glottal Closure Instants (GCI, also known as instants of significant excitation) and Vowel Onset Points (VOP). In general, most of the time scale modification methods attempt to vary the duration of speech segments uniformly over all regions. But it is observed that consonant regions and transition regions between a consonant and the following vowel, and between two consonant regions do not vary appreciably with speaking rate. The proposed method implements the duration modification without changing the durations of the transition and consonant regions. Vowel onset points are used to identify the transition and consonant regions. A VOP is the instant at which the onset of the vowel takes place, which corresponds to the transition from a consonant to the following vowel in most cases. The VOPs are computed using the Hilbert envelope of Linear Prediction (LP) residual. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations, like the onset of burst, in the case of nonvoiced speech. Manipulation of duration is achieved by modifying the duration of the LP residual with the help of instants of significant excitation as pitch markers. The modified residual is used to excite the time-varying filter whose parameters are derived from the original speech signal. Perceptual quality of the synthesized speech is found to be natural. Performance of the proposed method is compared with the method, where the duration of speech is modified uniformly over all regions. Samples of speech signals for different modification factors is available for listening at

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Speech-adaptive time-scale modification for computer assisted language-learning

Cited by 9 publications

References 7 publications

Broad phonetic classification using discriminative Bayesian networks

Broad phonetic classification using discriminative Bayesian networks

Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Duration modification using glottal closure instants and vowel onset points

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