Automatic analysis of dialect/language sets

Mehrabani, Mahnoosh; Hansen, John H. L.

doi:10.1007/s10772-014-9268-y

Cited by 18 publications

(11 citation statements)

References 26 publications

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“…Окремо згадаємо підклас систем другого типу, у яких використовується колекція словоформ. Призначенням таких систем є граматично-морфологічний аналіз, в якому у колекції представляється множина комбінацій словоформ, якій поставлена у відповідність множина граматичних міток [11], [19], [23]. За достатньо насиченої колекції джерелом похибок аналізу в цих системах є лише омонімія повної словоформи.…”

Section: огляд аналогівunclassified

“…. Альтернативою виразам (23), (24) може слугувати врахування у правилі (22) принципу мінімуму величини інформаційного неспрямованого розузгодження ( ) ( )…”

Section: нехайunclassified

“…Також припустимо, що під час розпізнавання цього ж сигналу за допомогою розв'язувального правила (22) вердикт був винесений на користь гіпотези ( ) питомі значення розв'язувальних статистик (23), (24), відповідно; 0 сзначення порогу (мінімальне значення коефіцієнту асиметрії значень ( 23) і (24) у правилі ( 22)), встановлюване залежно від максимально допустимої похибки ,0 0 0 0,…”

Section: нехайunclassified

See 2 more Smart Citations

Elements of Methodology of Precision Phonetic Analysis of Oral Phonograms

Danylchuk¹,

Kovtun²,

Nykytenko³

et al. 2022

VVPI

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Section: огляд аналогівunclassified

Section: нехайunclassified

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Elements of Methodology of Precision Phonetic Analysis of Oral Phonograms

Danylchuk¹,

Kovtun²,

Nykytenko³

et al. 2022

VVPI

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“…We separately mention the subclass of systems of the second type, which uses a collection of word forms. The purpose of such systems is grammatical and morphological analysis, in which the collection presents a set of combinations of word forms, which is matched by a set of grammatical labels [ 11 , 19 , 23 ]. With a sufficiently rich collection, the source of analysis errors in these systems is only the homonymy of the complete word form.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Argumentative Concept of Computational Phonetic Analysis of Speech Taking into Account Dialect and Individuality of Phonation

Kovtun

Семенов

2022

Entropy

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In this article, the concept (i.e., the mathematical model and methods) of computational phonetic analysis of speech with an analytical description of the phenomenon of phonetic fusion is proposed. In this concept, in contrast to the existing methods, the problem of multicriteria of the process of cognitive perception of speech by a person is strictly formally presented using the theoretical and analytical apparatus of information (entropy) theory, pattern recognition theory and acoustic theory of speech formation. The obtained concept allows for determining reliably the individual phonetic alphabet inherent in a person, taking into account their inherent dialect of speech and individual features of phonation, as well as detecting and correcting errors in the recognition of language units. The experiments prove the superiority of the proposed scientific result over such common Bayesian concepts of decision making using the Euclidean-type mismatch metric as a method of maximum likelihood and a method of an ideal observer. The analysis of the speech signal carried out in the metric based on the proposed concept allows, in particular, for establishing reliably the phonetic saturation of speech, which objectively characterizes the environment of speech signal propagation and its source.

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“…Prosodic features like pitch have been used to quantify difference in bird populations. In [12], O'Reilly et al used pitch contour micro-structure to measure similarity of bird calls and songs (inspired by dialect similarity measures used by Mehrabani et al in [13,14]). Pitch analysis is not only performed by engineers, zoologists have used it in their work too.…”

Section: Introductionmentioning

confidence: 99%

YIN-Bird: Improved Pitch Tracking for Bird Vocalisations

et al. 2016

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Pitch is an important property of birdsong. Accurate and automatic tracking of pitch for large numbers of recordings would be useful for automatic analysis of birdsong. Currently, pitch trackers such as YIN can work with carefully tuned parameters but the characteristics of birdsong mean those optimal parameters can change quickly even within a single song. This paper presents YIN-bird, a modified version of YIN which exploits spectrogram properties to automatically set a minimum fundamental frequency parameter for YIN. This parameter is continuously updated without user intervention. A ground truth dataset of synthetic birdsong with known fundamental frequency is generated for evaluation of YIN-bird. Listener tests from expert birders described the synthetic samples as "sounding like original & can hardly tell it is synthetic". Gross pitch error on whistles and trills were reduced by up to 4%. An analysis of nasal sounds shows the challenge in accurate pitch tracking for this syllable type.

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Automatic analysis of dialect/language sets

Cited by 18 publications

References 26 publications

Elements of Methodology of Precision Phonetic Analysis of Oral Phonograms

Elements of Methodology of Precision Phonetic Analysis of Oral Phonograms

Entropy-Argumentative Concept of Computational Phonetic Analysis of Speech Taking into Account Dialect and Individuality of Phonation

YIN-Bird: Improved Pitch Tracking for Bird Vocalisations

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