ModelTalker voice recorder

Yarrington, Debra; Gray, John; Pennington, Christopher; Bunnell, H. Timothy; Cornaglia, Allegra; Lilley, Jason; Nagao, Kyoko; Polikoff, James B.

doi:10.3115/1564144.1564152

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…The materials to record were presented on a computer screen in the Greek alphabet. The participants first went through a trial run by recording nine training sentences, which were meant to familiarize them with the task and the recording tool (Yarrington et al 2008). These sentences were not used in the analysis.…”

Section: Current Studymentioning

confidence: 99%

“…The recording software (Yarrington et al 2008) used for the experiment automatically produces an alignment of labels to recorded speech if provided with a phonetic transcription. The alignment tool uses a set of hidden Markov models trained on English; for details see DiCanio et al (2013; the alignment tool is referred to as ‘ hmalign ’).…”

Section: Current Studymentioning

confidence: 99%

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Exploring the front fricative contrast in Greek: A study of acoustic variability based on cepstral coefficients

Lilley¹,

Spinu²,

Athanasopoulou³

2020

Journal of the International Phonetic Association

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In the current study, we explore the factors underlying the well-known difficulty in acoustic classification of front nonsibilant fricatives (Maniwa, Jongman & Wade 2009, McMurray & Jongman 2011) by applying a novel classification method to the production of Greek speakers. The Greek fricative inventory [f v θ ð s z ç ʝ x ɣ] includes voiced and voiceless segments from five distinct places of articulation. Our corpus contains all of the Greek fricatives produced by 29 monolingual speakers, but our focus is on the distinction between the front nonsibilant fricatives [f v θ ð]. For comparison, we also discuss the other places of articulation where relevant. We apply a relatively novel classification method based on cepstral coefficients, previously successful in categorizing English obstruent bursts (Bunnell, Polikoff & McNicholas 2004), English vowels (Ferragne & Pellegrino 2010), Romanian fricatives (Spinu & Lilley 2016), and Russian fricatives (Spinu, Kochetov & Lilley 2018). For this study, fricative boundaries were automatically aligned using Hidden Markov Models (HMMs) and then manually checked. Six Bark-frequency cepstral coefficients (c0–c5) were extracted from 20-millisecond Hann windows. HMMs were used to divide the fricatives and adjacent vowels into three regions of internally minimized variance. A multinomial logistic regression analysis then used the mean cepstral coefficients from each region as predictors for classification by consonant identity. Our method yields highly successful classification rates, exceeding the performance of previous methods. We discuss these results in light of the differences of the phonemic distributions of fricatives between English and Greek.

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Section: Current Studymentioning

confidence: 99%

Section: Current Studymentioning

confidence: 99%

Exploring the front fricative contrast in Greek: A study of acoustic variability based on cepstral coefficients

Lilley¹,

Spinu²,

Athanasopoulou³

2020

Journal of the International Phonetic Association

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A comparison of cepstral coefficients and spectral moments in the classification of Romanian fricatives

Spinu

Lilley²

2016

Journal of Phonetics

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In this paper we explore two methods for the classification of fricatives. First, for the coding of the speech, we compared two sets of acoustic measures obtained from a corpus of Romanian fricatives: (a) spectral moments, and (b) cepstral coefficients. Second, we compared two methods of determining the regions of the segments from which the measures would be extracted. In the first method, the phonetic segments were divided into three regions of approximately equal duration. In the second method, Hidden Markov Models (HMMs) were used to divide each segment into three regions such that the variances of the measures within each region were minimized. The corpus we analyzed consists of 3,674 plain and palatalized word-final fricatives from four places of articulation, produced by 31 native speakers of Romanian (20 females). We used logistic regression to classify fricatives by place, voicing, palatalization status, and gender. We found that cepstral coefficients reliably outperformed spectral moments in all classification tasks, and that using regions determined by HMM yielded slightly higher correct classification rates than using regions of equal duration.

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Vowel recognition from continuous articulatory movements for speaker-dependent applications

Wang

Green

Samal

et al. 2010

2010 4th International Conference on Signal Processing and Communication Systems

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Abstract-A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels. This finding parallels previous empirical findings on tongue movements during vowels. The recognition algorithm was then used to drive an articulation-to-acoustics synthesizer. The synthesizer recognizes vowels from continuous input stream of tongue and lip movements and plays the corresponding sound samples in near real-time.

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ModelTalker voice recorder

Cited by 5 publications

References 2 publications

Exploring the front fricative contrast in Greek: A study of acoustic variability based on cepstral coefficients

Exploring the front fricative contrast in Greek: A study of acoustic variability based on cepstral coefficients

A comparison of cepstral coefficients and spectral moments in the classification of Romanian fricatives

Vowel recognition from continuous articulatory movements for speaker-dependent applications

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