Dialect Identification Using Spectral and Prosodic Features on Single and Ensemble Classifiers

Chittaragi, Nagaratna B.; Prakash, Ambareesh; Koolagudi, Shashidhar G.

doi:10.1007/s13369-017-2941-0

Cited by 32 publications

(6 citation statements)

References 37 publications

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“…Frequency indicates that how a sound wave vibrates i.e., the number of wavelengths in a particular unit of time. As shown in the below figure the first diagram contains low frequency made it contains only 3 waves in the unit of time 1sec while the second diagram contains 6 waves in the unit of time 1 sec.The high and low pitch siganls as shown in below figure [5].…”

Section: Pitchmentioning

confidence: 99%

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Comparison Of Different Feature Extraction Techniques In Telugu Dialects Identification

Shivaprasad¹

2021

TURCOMAT

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The Telugu language is one of the standard and historical languages in India. Like other spoken languages, the Telugu language also contains different dialects i.e., Telangana, Costa Andhra and Rayalaseema. The research in dialects identification (DI) work is very less as compared to other speech processing applications. For the research Dialect identification (DI) in Telugu language, database is very important, but there is no standard database. To carry out the research work in DI, we created the database of Telugu Language with different dialects and we anlyzed different feature extraction techniques to implement system. In this paper, we compare the performance of different models given by applying the different feature extraction techniques such as spatial, temporal, and prosodic features in Telugu dialect identification. We have applied different classification models i.e., K-Nearest Neighbour (K-NN), Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). It is observed that GMM model provides good accuracy with MFCC+∆MFCC+∆∆MFCC and MFCC+PITCH +LOUDNESS features compare to HMM model.

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Section: Pitchmentioning

confidence: 99%

“…It is the most commonly spoken language of the Dravidian language family [4] and one of the twenty-two scheduled languages of the Republic of India. It is also the fastest-growing language in the United States, where there is a significant Telugu-speaking population [5]. Around 10,000 pre-colonial inscriptions exist in the Telugu language.…”

Section: Introductionmentioning

confidence: 99%

Comparison Of Different Feature Extraction Techniques In Telugu Dialects Identification

Shivaprasad¹

2021

TURCOMAT

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“…After the front-end processing, the next step is feature extraction, which is discussed in the ensuing section. To extract spectral characteristics from speech signals, various techniques have been used in literature, but the most popular spectral feature extraction technique is MFCC, which has been widely used in all VPSs [64][65][66][67].…”

Section: Front-end Processingmentioning

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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“…Integrating prosodic features was also found to be useful in developing language identification tools (Chittaragi, 2018). For instance, Reddy (2013) extracted spectral and prosodic features to analyse language-specific information found in speech.…”

Section: Relationship To Previous Workmentioning

confidence: 99%

Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

Alsharhan¹,

Alnajem²

2021

SJKFU

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Developing natural language processing applications for Arabic must consider the different linguistic characteristics found in speech and translate those characteristics to script in order to reduce computational complexity and therefore reduce the word error rate (WER). Suprasegmental features are fundamental properties of speech that can enhance the performance of many natural speech processing applications. The present study considered stress as a prosodic feature comprising the prominence of syllables in speech by developing a tool that generated phonetic transcriptions and predicted the stress position. The generated transcription was used to create the phonetic dictionary necessary for developing an automatic speech recognition (ASR) system. This tool had to be accurate, linguistically motivated, and applicationally useful; therefore, the effectiveness of the generated stress-marked phonetic dictionary was tested by comparing the performance of a standard fixed dictionary-based system with that of one using the automatically generated dictionary. The research reported a 5.6% reduction in WER when using a dictionary with stress markers attached to each phone in the stressed syllable and a 3.5% reduction in WER when using a dictionary with stress markers assigned only to stressed vowels. These results encourage future studies to employ prosodic features of speech when developing different speech processing applications.

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Dialect Identification Using Spectral and Prosodic Features on Single and Ensemble Classifiers

Cited by 32 publications

References 37 publications

Comparison Of Different Feature Extraction Techniques In Telugu Dialects Identification

Comparison Of Different Feature Extraction Techniques In Telugu Dialects Identification

A Robust Approach for Speaker Identification Using Dialect Information

Developing a Stress Prediction Tool for Arabic Speech Recognition Tasks

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