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Deeply Korean read speech corpus 深度韩语阅读语料库

Deeply Korean read speech corpus 深度韩语阅读语料库

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Music Analysis Audio

about this resource:Recording environment: Studio apartment(moderate reverb), Dance studio(high reverb), Anechoic chambe......

数据结构 ? 281M

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    about this resource:

    • Recording environment: Studio apartment(moderate reverb), Dance studio(high reverb), Anechoic chamber(no reverb)

    • Device: iPhone x, Samsung Galaxy S7

    • Recording distance from the source: 0.4m, 2.0m, 4.0m

    • Volume(full set): ~3(~290) hours, ~2,000(~190,000) utterances, ~0.5(~107) GB

    • Format: 16kHz, 16-bit, mono

    • Language: Korean

    Pairs of Korean speakers reading scripts with 3 distinct text sentiments (negative, neutral, positive), with 3 distinct vocal sentiments (negative, neutral, positive), are recorded.
    The recordings took place in 3 different types of places, which are an anechoic chamber, studio apartment, and dance studio, of which the level of reverberation differs.
    And, in order to examine the effect of the distance of mic from the source and device, every experiment is recorded at 3 distinct distances with 2 types of smartphone, iPhone X, and Galaxy S7.

    The dataset is a subset(approximately 1%) of a much bigger dataset which were recorded under the same environment as this public dataset.
    This sample dataset only contains only a limited amount which contains a single pair of speaker recorded in 3 places, but only at single distance and with iPhone X.
    Please visit our website Deeply Inc., GitHub, or contact us for more details and access to the full set of the dataset with commercial license.

    Deeply makes products that anyone can use with audio AI technology and makes people's lives happier with those products. For more products and services, please visit Deeply Inc..

    Structure

    Korean_Read_Speech_Corpus.json
    
    {'AirbnbStudio': 
                    {'sub100100a00000': {'text_sentiment': 0,
                                        'voice_sentiment': -1,
                                        'subjectID': 'sub1001',
                                        'speaker': 'a',
                                        'age': 20,
                                        'sex': 0,
                                        'noise': 0,
                                        'location': 0,
                                        'distance': 0,
                                        'device': 0,
                                        'text': '저 식당 음식이 정말 맛있나 봐요.',
                                        'text_code': 'aa0',
                                        'rms': 0.024304501712322235,
                                        'length': 2.71825},
                      ...
                      },
        ...
    }Text sentiment : {-1: negative, 0: neutral, 1: positive}  
    Vocie sentiment: {-1: negative, 0: neutral, 1: positive}  
    Subject ID     : unique 'sub + 4-digit' key allocated to each subject group  
    Speaker        : unique key allocated to each indiivdual in the subject group.  
    Sex            : {0: Female, 1: Male}  
    Noise          : {0: Noiseless, 1: Indoor noise, 2: Outdoor noise, 3: Both indoor/outdoor noise}  
    Location       : {0: Studio apartment, 1: Dance studio, 2: Anechoic chamber}  
    Distance       : {0: 0.4m, 1: 2.0m, 2: 4.0m}  
    Device         : {0: iPhone X, 1: Galaxy S7}  
    Text           : the content of the script  
    Text code      : unique key allocated to each line of the script  
    Rms            : root mean square value of the signal  
    Length         : length of the signal in secods

    You can cite the data as follows:

    @misc{deeply_corpus_kor,
      title={{Deeply Korean read speech corpus}},
      author={Deeply Inc.},
      year={2021},
      url={https://github.com/deeplyinc/Korean-Read-Speech-Corpus}
    }

       Contact
       Tel:   (+82) 70-7459-0704
       Web:   http://deeplyinc.com/
       Email: contact@deeplyinc.com


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