Improved accent classification combining phonetic vowels with acoustic features

Ge, Zhenhao

doi:10.1109/cisp.2015.7408064

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…For instance, research conducted on the acoustics of Brazilian and European Portuguese dialects revealed substantial disparities in intrinsic vowel attributes, such as F1, F2, pitch, and duration [36]. Another approach has been developed that improves dialect classification performance by fusing phonetic data with vowel acoustic properties [37]. Similar studies have examined how the fifteen normal Dutch vowels are pronounced in different Dutch and Belgian accents by looking at the initial three formants, fundamental frequencies, and duration characteristics [39].…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

Assamese Dialect Identification Using Static and Dynamic Features from Vowel

Das,

Bhattacharjee

2024

JAIT

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Implementation of Machine Learning-Aided Speech Analysis for Speaker Accent Identification Applied to Audio Forensics

Mahesh

Raj

Nersisson

2022

Advances in Digital Crime, Forensics, and Cyber Terrorism

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Accent recognition as a subset of speech recognition is crucial in audio forensics as it provides the authenticity of the speech that can be presented to the judicature as evidence. The challenge here is to design a speaker accent identifying system that can provide significant information to match with the standards of the court of justice. This process requires robust descriptors to represent speech signal with good discrimination ability. This chapter proposes to use Mel Frequency Cepstral Coefficients to identify and represent the human utterances by transforming the frequency from normal scale to Mel scale. The work utilized support vector machine, k nearest neighbors, XG boost, linear discriminant analysis, quadratic discriminant analysis, and decision tree algorithms to recognize the accent of the speaker. The experiment conducted on the accent recognition dataset demonstrated the ability of Mel Frequency Cepstral Coefficients and kNN classifier in identifying and discriminating six accents belonging to different speakers with better accuracy.

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Improved accent classification combining phonetic vowels with acoustic features

Cited by 2 publications

References 14 publications

Assamese Dialect Identification Using Static and Dynamic Features from Vowel

Assamese Dialect Identification Using Static and Dynamic Features from Vowel

Implementation of Machine Learning-Aided Speech Analysis for Speaker Accent Identification Applied to Audio Forensics

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