Metadata-Version: 1.1
Name: myspokenlanguagedetection
Version: 3
Summary: Spoken language identification with CNN and RNN - Early Stage
Home-page: https://github.com/Shahabks/myspokenlanguageid
Author: Shahab Sabahi
Author-email: sabahi.s@mysol-gc.jp
License: MIT
Description: Spoken Language Identification is the process of determining and classifying natural language 
        from a given content and dataset. Employing an acoustic model and a language model, Data of 
        audio files is processed to extract useful features for performing Machine Learning. 
        The acoustic features for SPOKEN LANGUAGE IDENTIFICATION are namely standard features such 
        as Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), while for the 
        language features the Gaussian Mixture Model (GMM) and the i-vector based framework are 
        used. 
        
        However, the Machine Learning process based on extract features remains a challenge. 
        Optimisation needs to be improved in order to capture embedded knowledge on the extracted 
        features. CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks) and ELM (the 
        Extreme Learning Machine) are promising as effective learning architectures used to perform 
        classification and further complex analysis and are extremely useful to train a single 
        hidden layer neural network. However, by now, the learning process of these models is not 
        entirely effective due to the selection methods of weights within the input hidden layer.
        
        myspokenlanguagedetection is a preliminary package structured for SPOKEN LANGUAGE 
        IDENTIFICATION based on standard feature extraction
        and CNN and RNN. An optimisation approach was employed as the benchmark and improved by 
        altering the selection phase of the optimisation process. The selection process is performed
        incorporating deferent methods. The results are generated based on SPOKEN LANGUAGE 
        IDENTIFICATION with the datasets created from eighteen different languages. The results of 
        the study indicate the performance of Machine Learning highly correlated with the soundness 
        of architecture of Neural Networks and co-existence of acoustic and language models.
        
        THIS version of myspokenlanguagedetection was trained to detect "French", "English", "Spanih", 
        "Italian", "Deutsch", "Russian", "Portuguese", "Swedish", and "Japanese" and to some lower 
        extent other 40 languages. We will complete the machine training sessions for morelanguages 
        along with increaing the accuracy of the languages identification process.
        
        =============
        Installation
        =============
        Mypokenlanguage can be installed like any other Python library, using (a recent version of) the
        Python package manager pip, on Linux, macOS, and Windows:
        
        ------------------pip install mypokenlanguage
        				
        or, to update your installed version to the latest release:
        ------------------- pip install -u mypokenlanguage 	---------------------------------
        
        Recording files must be 15 secs or longer of audio and in *.wav PCM/LPCM format, recorded at 48 kHz 
        sample frame and 24-32 bits of resolution or AIFF, AIFF-C, FLAC: must be native FLAC format; 
        OGG-FLAC is not supported.
        
        please check out https://github.com/Shahabks/myspokenlanguageid    
        
        myspokenlanguagedetection was developed by MYOLUTIONS Lab in Japan. It is part of New Generation of 
        Voice Recognition and Acoustic & Language modelling Project in MYSOLUTIONS Lab. That is planned to 
        enrich the functionality of myspokenlanguagedetection by adding more advanced functions.
Keywords: speech signal processing,Naural Langguage Processing and Understanding
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
