Significance of GMM-UBM based Modelling for Indian Language Identification

Kumar, Vijay; Vydana, Hari Krishna; Vuppala, Anil Kumar

doi:10.1016/j.procs.2015.06.027

Cited by 16 publications

(8 citation statements)

References 8 publications

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“…The state-of-the-art SLID systems used Vector Quantization (VQ), Gaussian Mixture Model (GMM) [4,17], Support Vector Machine (SVM) [18][19][20], Hidden Markov Model (HMM) [21], Artificial Neural Network (ANN) [4,21,22], and Random Forest (RF) [3,6]. The modern endto-end language recognition models based on deep learning (DL) algorithms improves the performance by increasing the data set requirement and do not perform well for small data set.…”

Section: Review Of Related Workmentioning

confidence: 99%

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Bakshi

Kopparapu

2021

SN COMPUT. SCI.

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Paper presents novel duration normalized feature selection technique and two-step modified hierarchical classifier to improve the accuracy of spoken language identification (SLID) using Indian languages for duration mismatched condition. Feature selection averages random forest-based importance vectors of open SMILE features of different duration utterances. Although it improves the SLID system's accuracy for mismatched training and testing durations, the performance is significantly reduced for short-duration utterances. A cascade of inter-family and intra-family classifiers with an additional class to improve false language family estimation. All India Radio data set with nine Indian languages and different utterance durations was used as speech material. Experimental results showed that 150 optimal features with the proposed modified hierarchical classifier showed the highest accuracy of 96.9% and 84.4% for 30 s and 0.2 s utterances for the same train-test duration. However, we achieved an accuracy of 98.3% and 61.9% for 15 and 0.2 s test duration when trained with 30 s duration utterance. Comparative analysis showed a significant improvement in accuracy than several SLID systems in the literature.

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Section: Review Of Related Workmentioning

confidence: 99%

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Bakshi

Kopparapu

2021

SN COMPUT. SCI.

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“…It was reported that the performance of MFCC based systems decreases with decreasing frame size [9,10]. Classifiers like Hidden Markov Model (HMM) [12], vector quantization (VQ) [6], support vector machine (SVM) [3,13,14], artificial neural network (ANN) [15,16], and Gaussian mixture model (GMM) [15][16][17] have been reported to model feature vectors in SLID systems. One of the simplest techniques used for the SLID system is GMM-UBM.…”

Section: Introductionmentioning

confidence: 99%

“…In this method, maximum likelihood estimation is used to train the language model, and maximum a posterior (MAP) estimation is used to adapt the UBM model. The speech sample is a series of the independent spectral feature vector, and GMM mathematically models these features with UBM adaption known as GMM-UBM supervectors carries spectral characteristics [10][11][12][13][14][15][16][17][18]. These features are adapted to UBM using the MAP estimation algorithm to obtain utterance-based GMM [19].…”

Section: Introductionmentioning

confidence: 99%

A GMM supervector approach for spoken Indian language identification for mismatch utterance length

Bakshi

Kopparapu²

2021

Bulletin EEI

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Gaussian mixture model-universal background model (GMM UBM) supervectors are used to identify spoken Indian languages. The supervectors are calculated from short-time MFCC, its first and sec derivatives. The UBM builds a generalized Indian language model, and mean adaptation transforms it to a duration normalized language-specific GMM. Multi-class support vector machine and artificial neural network classifiers are used to identify language labels from the supervectors. Experimental evaluations are performed using 30 sec speech utterances from nine Indian languages comprised five Indo-Aryan and four Dravidian languages, extracted from all India radio broadcast news data-set. Eight smaller duration data-sets were manually derived to study the effect of training and test duration mismatch. In mismatch conditions, identification accuracy decreases with a decrease in test and train utterance duration. Investigations showed that the 32-mixture model with ANN classifier has optimal performance.

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“…Accent provides information about geographical and territorial origin of speakers [5,6]. Other applications of accent are telephone-based assistant systems, telephone banking, voice mail, voice dialling, and e-learning [7,8]. This paper deals with extraction of accent from Urdu speech signals for forensic speaker recognition.…”

Section: Introductionmentioning

confidence: 99%

“…GMM-UBM is used as a classifier.The objective of this comparison is to identify the best features for accent recognition. The GMM-UBM is trained using different Gaussian mixture components i.e 2,4,8,16,. 32, 64, 128, and 256.…”

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confidence: 99%

Extracting accent information from Urdu speech for forensic speaker recognition

Tahir¹,

Saleem²,

Ahmad³

2019

Turk J Elec Eng & Comp Sci

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This paper presents a new method for extraction of accent information from Urdu speech signals. Accent is used in speaker recognition system especially in forensic cases and plays a vital role in discriminating people of different groups, communities and origins due to their different speaking styles. The proposed method is based on Gaussian mixture model-universal background model (GMM-UBM), mel-frequency cepstral coefficients (MFCC), and a data augmentation (DA) process. The DA process appends features to base MFCC features and improves the accent extraction and forensic speaker recognition performances of GMM-UBM. Experiments are performed on an Urdu forensic speaker corpus. The experimental results show that the proposed method improves the equal error rate and the accuracy of GMM-UBM by 2.5% and 3.7%, respectively.

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Significance of GMM-UBM based Modelling for Indian Language Identification

Cited by 16 publications

References 8 publications

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

A GMM supervector approach for spoken Indian language identification for mismatch utterance length

Extracting accent information from Urdu speech for forensic speaker recognition

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