Analysis and modeling of acoustic information for automatic dialect classification

Agrawal, S.; Jain, Aruna; Sinha, Shweta

doi:10.1007/s10772-016-9351-7

Cited by 18 publications

(4 citation statements)

References 21 publications

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“…Analysis of English, French and German languages indicate that speakers significantly differ in their intonation slope (Grover, Jamieson & Dobrovolsky, 1987). This correlation has also been shown for certain Indian dialects (Agrawal, Jain & Sinha, 2016).…”

Section: Introductionsupporting

confidence: 52%

An integrated dialect analysis tool using phonetics and acoustics

Jones

Nadjibzadeh

Karoly

et al. 2019

Lingua

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This study aimed to verify a computational phonetic and acoustic analysis tool created in the MATLAB environment. A dataset was obtained containing 3 broad American dialects (Northern, Western and New England) from the TIMIT database using words that also appeared in the Swadesh list. Each dialect consisted of 20 speakers uttering 10 sentences. Verification using phonetic comparisons between dialects were made by calculating the Levenshtein distance in Gabmap and the proposed software tool. Agreement between the linguistic distances using each analysis method was found. Each tool showed increasing linguistic distance as a function of increasing geographic distance, in a similar shape to Seguy's curve. The proposed tool was then further developed to include acoustic characterisation capability of inter dialect dynamics. Significant variation between dialects was found for the pitch, trajectory length and spectral rate of change for 7 of the phonetic vowels investigated. Analysis of the vowel area using the 4 corner vowels indicated that for male speakers, geographically closer dialects have smaller variations in vowel space area than those further apart. The female utterances did not show a similar pattern of linguistic distance likely due to the lack of one corner vowel /u/, making the vowel space a triangle.

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Section: Introductionsupporting

confidence: 52%

An integrated dialect analysis tool using phonetics and acoustics

Jones

Nadjibzadeh

Karoly

et al. 2019

Lingua

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“…For the purpose of identifying the acoustic factors associated with these dialects, an ANOVA test is executed on the vowel sound units in Assamese. Agrawal et al [60] examine the variations in Hindi dialects by employing Analysis of Variance (ANOVA) on acoustic attributes including formant frequency, pitch, pitch slope, duration, and intensity in vowel sounds. They employ a kernel-based Support Vector Machine (SVM) to gauge the capacity of these acoustic features to distinguish dialects, achieving a classification accuracy of 66.97%.…”

Section: Statistical Evaluation Of Featuresmentioning

confidence: 99%

Assamese Dialect Identification Using Static and Dynamic Features from Vowel

Das,

Bhattacharjee

2024

JAIT

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This paper introduces a novel method for identifying Assamese dialects by analyzing the acoustic and prosodic aspects of vowel sounds in speech signals. The distinctive characteristics of these dialects are captured through the use of acoustic parameters such as formants (F1, F2, and F3), as well as prosodic features like energy, fundamental frequency (F0), and duration. To evaluate this approach, a comprehensive vowel speech corpus is collected from native Assamese speakers representing four different dialectal regions. Frame-level statistical features are extracted from vowel sounds, while temporal dynamic features are obtained from steady-state vowel segments. The data collection process involves using a phonetically rich script to record both read and spontaneous speech interactions from speakers of the four dialects. Various classification methods, including three decision tree-based classifiers, i.e., Random Forest (RF), Extreme Random Forest (ERF), and Extreme Gradient Boosting (XGB), are applied to distinguish the four dialects. The performance of each feature, whether static or dynamic, is individually evaluated. The study reveals that the identification of Assamese dialects is influenced by factors such as speech length, intensity, pitch, and formant frequencies. To assess the significance of these features in distinguishing dialects and to measure their combined impact on the identification system, single-factor Analysis of Variance (ANOVA) tests are conducted. Notably, when static features are combined with the Extreme Random Forest (ERF) ensemble model, the overall accuracy of dialect identification reaches 77%. This research demonstrates the efficacy of using acoustic and prosodic features to accurately classify Assamese dialects, shedding light on the subtle variations within them. In summary, this paper provides a robust framework for Assamese dialect identification and contributes to our understanding of dialect discrimination, paving the way for more advanced dialect identification systems.

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“…e second type deals with the automatic accent and dialect identification. In this category, various dialect classification [30,31], dialect identification [32], and dialect detection [33] systems have been presented.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, the acoustic approach is based on the idea that different accents or dialects of a language differ in terms of spectral characteristics. In this approach, some sort of spectral and prosodic features such as formant frequencies, pitch, pitch slope, duration, and intensity for vowel sounds are used to discriminate among different accents and dialects of a language [30].…”

Section: Related Workmentioning

confidence: 99%

A Robust Approach for Speaker Identification Using Dialect Information

Shah

Moinuddin²,

Khan

2022

Applied Computational Intelligence and Soft Computing

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The present research is an effort to enhance the performance of voice processing systems, in our case the speaker identification system (SIS) by addressing the variability caused by the dialectical variations of a language. We present an effective solution to reduce dialect-related variability from voice processing systems. The proposed method minimizes the system’s complexity by reducing search space during the testing process of speaker identification. The speaker is searched from the set of speakers of the identified dialect instead of all the speakers present in system training. The study is conducted on the Pashto language, and the voice data samples are collected from native Pashto speakers of specific regions of Pakistan and Afghanistan where Pashto is spoken with different dialectal variations. The task of speaker identification is achieved with the help of a novel hierarchical framework that works in two steps. In the first step, the speaker’s dialect is identified. For automated dialect identification, the spectral and prosodic features have been used in conjunction with Gaussian mixture model (GMM). In the second step, the speaker is identified using a multilayer perceptron (MLP)-based speaker identification system, which gets aggregated input from the first step, i.e., dialect identification along with prosodic and spectral features. The robustness of the proposed SIS is compared with traditional state-of-the-art methods in the literature. The results show that the proposed framework is better in terms of average speaker recognition accuracy (84.5% identification accuracy) and consumes 39% less time for the identification of speaker.

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Analysis and modeling of acoustic information for automatic dialect classification

Cited by 18 publications

References 21 publications

An integrated dialect analysis tool using phonetics and acoustics

An integrated dialect analysis tool using phonetics and acoustics

Assamese Dialect Identification Using Static and Dynamic Features from Vowel

A Robust Approach for Speaker Identification Using Dialect Information

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