RETRACTED ARTICLE: Dialect recognition from Telugu speech utterances using spectral and prosodic features

Shivaprasad, S.; Sadanandam, M.

doi:10.1007/s10772-021-09854-8

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…This coding could improve the accuracy of Chinese DSR and reduce the burden on an AM to a certain extent. HMM is a commonly used classical method to construct the AM (Shivaprasad and Sadanandam 2021). Dialect recognition leveraging HMM can model the smallest unit of a dialect and generate a corresponding observation state to calculate the phonetic unit set of the hidden state repeatedly and iteratively.…”

Section: Dialect Dictionary Language Model Acoustic Modelmentioning

confidence: 99%

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: Dialect Dictionary Language Model Acoustic Modelmentioning

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

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“…At the segmental level, the dialect specific information can be identified as a unique sequence of the shapes of the vocal tract for producing the sound units [21]. The challenge of the dialect recognition system is to differentiate the dialects of standard language because there exists a lot of similarities between dialects of language [25]. Basically, experiences have proven that speaking in native accent with ASR systems typically ends up with not much success, so such ASR systems can still be improved [24].…”

Section: Marathi Dialect Recognitionmentioning

confidence: 99%

Text-Independent Automatic Dialect Recognition of Marathi Language using Spectro-Temporal Characteristics of Voice

Mulla¹,

Kumar²

2022

IJRITCC

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Text-independent dialect recognition system is proposed in this paper for Marathi language. India is rich in language varieties. Each language in turn has its unique dialect variations. Maharashtra has Marathi as official language and for Goa it is a co-official language . In literature there are very few studies available for Indian language recognition and then their respective dialect recognition. So research work available for regional languages such as Marathi is extremely limited. As a part of research work, an attempt is made to generate a case study of a low resourced Marathi language dialect recognition system. The study was carried out using Marathi speech data corpus provided by Linguistic Data Consortium for Indian Language (LDC- IL). This corpus includes four major dialects of Marathi speakers. The efficiency and performance evaluation of the explored spectral (rhythmic) and temporal features are carried out to perform classification tasks. We investigated the performance of six different classifiers; K-nearest neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) classifier , Stochastic Gradient Descent (SGD) classifier and Ridge Classifier (RC). Experimental results have demonstrated that the RC classifier worked well with 84.24% of accuracy for fifteen spectral and temporal features. With twelve MFCCs it has been observed that SGD has outperformed among all classifiers with accuracy of 80.63%. For further study, a prominent feature subset as a part of dimensionality reduction has been identified using chi square, mutual information and ANOVA-f test. In this chi-square based feature extraction method has proven to be the best over over mutual information and ANOVA f-test.

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