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        "author": "Shahab Sabahi",
        "author_email": "sabahi.s@mysol-gc.jp",
        "bugtrack_url": null,
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        "description": "*** Version-5 release with accuracy improvement *** \nThis program may take a minute or so to get results showed on the screen,\nplease be patient.\n\nSpoken Language Identification is the process of determining and classifying natural language \nfrom a given content and dataset. Employing an acoustic model and a language model, Data of \naudio files is processed to extract useful features for performing Machine Learning. \nThe acoustic features for SPOKEN LANGUAGE IDENTIFICATION are namely standard features such \nas Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), while for the \nlanguage features the Gaussian Mixture Model (GMM) and the i-vector based framework are \nused. \n\nHowever, the Machine Learning process based on extract features remains a challenge. \nOptimisation needs to be improved in order to capture embedded knowledge on the extracted \nfeatures. CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks) and ELM (the \nExtreme Learning Machine) are promising as effective learning architectures used to perform \nclassification and further complex analysis and are extremely useful to train a single \nhidden layer neural network. However, by now, the learning process of these models is not \nentirely effective due to the selection methods of weights within the input hidden layer.\n\nmyspokenlanguagedetection is a preliminary package structured for SPOKEN LANGUAGE \nIDENTIFICATION based on standard feature extraction\nand CNN and RNN. An optimisation approach was employed as the benchmark and improved by \naltering the selection phase of the optimisation process. The selection process is performed\nincorporating deferent methods. The results are generated based on SPOKEN LANGUAGE \nIDENTIFICATION with the datasets created from eighteen different languages. The results of \nthe study indicate the performance of Machine Learning highly correlated with the soundness \nof architecture of Neural Networks and co-existence of acoustic and language models.\n\nTHIS version of myspokenlanguagedetection was trained to detect \"French\", \"English\", \"Spanish\", \n\"Italian\", \"Deutsch\", \"Russian\", \"Portuguese\", \"Swedish\", and \"Japanese\" and to some lower \nextent other 40 languages. We will complete the machine training sessions for more languages \nalong with increasing the accuracy of the languages identification process.\n\n=============\nInstallation\n=============\nmyspokenlanguagedetection can be installed like any other Python library, using (a recent version of) the\nPython package manager pip, on Linux, macOS, and Windows:\n\n------------------pip install myspokenlanguagedetection\n\nor, to update your installed version to the latest release:\n------------------- pip install -u myspokenlanguagedetection \t---------------------------------\n\nRecording files must be 25 sec. or longer of audio and in *.wav PCM/LPCM format, recorded at 48 kHz \nsample frame and 24-32 bits of resolution or AIFF, AIFF-C, FLAC: must be native FLAC format; \nOGG-FLAC is not supported.\n\nplease check out https://github.com/Shahabks/myspokenlanguageid    \n\nmyspokenlanguagedetection was developed by MYOLUTION Lab in Japan. It is part of New Generation of \nVoice Recognition and Acoustic & Language modeling Project in MYSOLUTION Lab. That is planned to \nenrich the functionality of myspokenlanguagedetection by adding more advanced functions.\n\n",
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        "keywords": "speech signal processing,Natural Language Processing and Understanding",
        "license": "MIT",
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        "name": "myspokenlanguagedetection",
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        "summary": "Spoken language identification with CNN and RNN - Improved Version: accuracy up",
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