scholarly journals HMM Based Language Identification from Speech Utterances of Popular Indic Languages Using Spectral and Prosodic Features

2021 ◽  
Vol 38 (2) ◽  
pp. 521-528
Author(s):  
Manchala Sadanandam
Keyword(s):  
Reference Model ◽  
Simple Procedure ◽  
Test Sample ◽  
Language Identification ◽  
Identification System ◽  
Human Beings ◽  
Indian Languages ◽  
Prosodic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Feature Vectors

Language identification system (LID) is a system which automatically recognises the languages of short-term duration of unknown utterance of human beings. It recognises the discriminate features and reveals the language of utterance that belongs to. In this paper, we consider concatenated feature vectors of Mel Frequency Cepstral Coefficients (MFCC) and Pitch for designing LID. We design a reference model one for each language using 14-dimensional feature vectors using Hidden Markov model (HMM) then evaluate against all reference models of listed languages. The likelihood value of test sample feature vectors given in the evaluation is considered to decide the language of unknown utterance of test speech sample. In this paper we consider seven Indian languages for the experimental set up and the performance of system is evaluated. The average performance of the system is 89.31% and 90.63% for three states and four states HMM for 3sec test speech utterances respectively and also it is also observed that the system gives significant results with 3sec test speech for four state HMM even though we follow simple procedure.

scholarly journals GMM-UBM Based Modeling for Language Identification using New Feature Vectors

2020 ◽  
Vol 9 (4) ◽  
pp. 3034-3039
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Language Identification ◽  
Training Data ◽  
Background Model ◽  
Indian Languages ◽  
Feature Vectors ◽  
Universal Background Model ◽  
New Feature

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.

A hierarchical language identification system for Indian languages

2012 ◽  
Vol 22 (3) ◽  
pp. 544-553 ◽  
Author(s):  
S. Jothilakshmi ◽  
V. Ramalingam ◽  
S. Palanivel
Keyword(s):  
Language Identification ◽  
Identification System ◽  
Indian Languages

New Feature Vectors for Language Identification Using Deep Neural Networks

2018 ◽  
Vol 6 (9) ◽  
pp. 157
Author(s):  
A. Nagesh
Keyword(s):  
Neural Networks ◽  
Automatic Speech Recognition ◽  
Deep Neural Networks ◽  
Language Identification ◽  
Identification System ◽  
Frequency Of Occurrence ◽  
Probability Vector ◽  
Feature Vectors ◽  
New Form ◽  
New Feature

The impressive performance of neural networks (NNs) for automatic speech recognition has motivated us to use for language identification (LID). In this paper, a new features based language identification system using neural network is presented. The new feature vectors are extracted based on the principle the frequency of occurrence phonemes is different among the languages. In this new form of feature vectors, the feature vectors are represented as a probability vector instead of scalar value. Because of this these new form of feature vectors, the DNN classifier classify the languages under consideration accurately.

scholarly journals Development of Indian Spoken Language Identification System for Two Languages using MFCC Feature with Deep Neural Network

2020 ◽  
Vol 9 (7S) ◽  
pp. 43-45
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Language Translation ◽  
Spoken Language ◽  
Language Identification ◽  
Classification Model ◽  
Identification System ◽  
Speech Sample ◽  
Indian Languages ◽  
Language Recognition

Language is the ability to communicate with any person. Approximate number of spoken languages are 6500 in the world. Different regions in a world have different languages spoken. Spoken language recognition is the process to identify the language spoken in a speech sample. Most of the spoken language identification is done on languages other than Indian. There are many applications to recognize a speech like spoken language translation in which the fundamental step is to recognize the language of the speaker. This system is specifically made to identify two Indian languages. The speech data of various news channels is used that is available online. The Mel Frequency Cepstral Coefficients (MFCC) feature is used to collect features from the speech sample because it provides a particular identity to the different classes of audio. The identification is done by using MFCC feature in the Deep Neural Network. The objective of this work is to improve the accuracy of the classification model. It is done by making changes in several layers of the Deep Neural Network.

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

Trafficking in human beings in Spain: What do the data on detected victims tell us?

2021 ◽  
pp. 147737082199733
Author(s):  
Carolina Villacampa ◽  
Mª Jesús Gómez ◽  
Clàudia Torres
Keyword(s):  
Quantitative Research ◽  
Sexual Exploitation ◽  
Identification System ◽  
Human Beings ◽  
Prevention Programmes ◽  
Trafficking In Human Beings ◽  
Research Period

Although trafficking in human beings was criminalized in Spain in 2010, data on this phenomenon are scarce and incomplete, consisting only of cases formally identified by police as having a very clear bias to trafficking for sexual exploitation. In an effort to increase empirical understanding, in 2019 we undertook quantitative research by gathering information on cases detected during 2017 and 2018. A questionnaire was distributed online to 757 stakeholders who could potentially have come across victims of trafficking. The 150 responses obtained provide valuable information about the number of victims, their profile, the dynamics of trafficking and the types of exploitation they suffered. The number of victims detected during the research period ( n = 7448) is far higher than those officially identified ( n = 458), which indicates that official cases may represent only the tip of the iceberg and point to the necessity of adopting measures to improve the identification system. Findings also show differences in victims’ profiles, victimization dynamics and forms of exploitation depending on the type of trafficking that could be taken into account when designing intervention and prevention programmes in this matter.

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