An Automated Classification System Based on Regional Accent

Guntur, Radha Krishna; Ramakrishnan, K. K.; Kumar, Mittal Vinay

doi:10.1007/s00034-021-01948-7

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…VAD can not only be used to mark the starting point and end point of dialect speech signals but also to distinguish between dialect noise and sound areas. In addition, VAD technology can deal with conversion problems effectively (Guntur et al 2022) when processing speech signals of Chinese dialects. Therefore, Liu et al (2021) presented a dialect and Mandarin interaction system with the VAD method to eliminate the speech problem of both.…”

Section: Signal Pre-processingmentioning

confidence: 99%

“…Therefore, the study of the acoustic characteristics of a dialect is one of the key tasks in the process of dialect recognition. To obtain dialect speech signals that are closer to human hearing, most current methods utilize cepstral features or spectral coefficients to represent speech signal features according to the acoustic characteristics of the dialect (Guntur et al 2022). The composition of the dialect recognition model adds a dialect dictionary based on the acoustic model (AM) and the LM (Ali et al 2022).…”

Section: Introductionmentioning

confidence: 99%

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Chinese dialect speech recognition: a comprehensive survey

Li,

Mai,

Wang

et al. 2024

Artif Intell Rev

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As a multi-ethnic country with a large population, China is endowed with diverse dialects, which brings considerable challenges to speech recognition work. In fact, due to geographical location, population migration, and other factors, the research progress and practical application of Chinese dialect speech recognition are currently at different stages. Therefore, exploring the significant regional heterogeneities in specific recognition approaches and effects, dialect corpus, and other resources is of vital importance for Chinese speech recognition work. Based on this, we first start with the regional classification of dialects and analyze the pivotal acoustic characteristics of dialects, including specific vowels and tones patterns. Secondly, we comprehensively summarize the existing dialect phonetic corpus in China, which is of some assistance in exploring the general construction methods of dialect phonetic corpus. Moreover, we expound on the general process of dialect recognition. Several critical dialect recognition approaches are summarized and introduced in detail, especially the hybrid method of Artificial Neural Network (ANN) combined with the Hidden Markov Model(HMM), as well as the End-to-End (E2E). Thirdly, through the in-depth comparison of their principles, merits, disadvantages, and recognition performance for different dialects, the development trends and challenges in dialect recognition in the future are pointed out. Finally, some application examples of dialect speech recognition are collected and discussed.

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Section: Signal Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chinese dialect speech recognition: a comprehensive survey

Li,

Mai,

Wang

et al. 2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…[15] tried to classify Arabic and Indian accents via Support Vector Machines (SVMs) based on MFCCs, which showed 75% to 97.5% accuracy with high precision and recall. [16] compared Gaussian Mixture Models (GMM), GMM-Universal Background Model (GMM-UBM), and i-vector in classifying Dravidian accented English. [17] used convolutional neural networks (CNNs) and Gated Recurrent Units (GRUs) to classify Ao's accents with approximately 6 hours of speech.…”

Section: Introductionmentioning

confidence: 99%

Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective

Zeng¹,

Chong²,

Zhou³

et al. 2022

Preprint

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Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based pretrained models have achieved superb performances in both areas. However, most accent classification tasks focused on classifying different kinds of English accents and little attention was paid to geographically-proximate accent classification, especially under a low-resource setting where forensic speech science tasks usually encounter. In this paper, we explored three main accent modelling methods combined with two different classifiers based on 105 speaker recordings retrieved from five urban varieties in Northern England. Although speech representations generated from pretrained models generally have better performances in downstream classification, traditional methods like Mel Frequency Cepstral Coefficients (MFCCs) and formant measurements are equipped with specific strengths. These results suggest that in forensic phonetics scenario where data are relatively scarce, a simple modelling method and classifier could be competitive with state-of-the-art pretrained speech models as feature extractors, which could enhance a sooner estimation for the accent information in practices. Besides, our findings also cross-validated a new methodology in quantifying sociophonetic changes.

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