Mobiusi

Group Communication Chinese Voice Dialogue Dataset

#Speech-to-text conversion #dialogue system training #voiceprint recognition #Speech recognition #natural language processing #intelligent assistants
  • 500 hours
  • 1.4G
  • WAV
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-02-04

AI Analysis & Value Prop

In modern society, speech recognition technology is gradually integrating into people's daily lives. However, most speech recognition systems are targeted at high-resource languages, while recognition in low-resource languages still faces significant challenges in the industry. Current solutions mainly rely on transfer learning from similar languages, but results are unsatisfactory. The group communication voice dialogue dataset aims to overcome this limitation, focusing on speech recognition and analysis for low-resource languages. Data collection uses professional-grade recording equipment in a quiet and undisturbed environment to ensure clear voice quality. Multiple rounds of annotation and consistency checks are key quality control measures, with the annotation team comprising 50 linguists and speech recognition experts. Data preprocessing includes noise filtering, audio segmentation, and speech feature extraction, with the final storage in WAV format for clear and manageable structure.

Dataset Insights

Sample Examples

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  • 83db1819469affd004e8ef1230a5aad7.wav
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Technical Specifications

FieldTypeDescription
file_namestringFile name
durationstringDuration
audio_ratestringAudio sample rate
audio_channelstringAudio channel
languagestringThe language used in the audio conversation.
speaker_countintThe number of distinct speakers participating in the conversation.
accentstringThe accent characteristic of the speakers.
dialogue_topicstringThe main topic discussed in the audio.
background_noise_levelstringThe pronounced level of environmental background noise (e.g., low, medium, high).
speech_emotionstringThe emotional characteristics of the speakers during the conversation (e.g., angry, happy, calm).
dialogue_turnsintThe number of turns or instances of speaking within the complete conversation.
speech_ratefloatThe rate of speech by the speakers during the conversation (words per minute).
transcriptiontextThe text transcription of the audio content.

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What is the primary use of the Group Communication Speech Dialogue Dataset?
The primary use of the Group Communication Speech Dialogue Dataset is to enhance speech recognition capabilities in low-resource languages.
Which industry domain does this dataset belong to?
This dataset belongs to the general daily industry domain.
What type of dataset is the Group Communication Speech Dialogue Dataset?
The Group Communication Speech Dialogue Dataset is a type of low-resource language dataset.
What role do audio data play in the Group Communication Speech Dialogue Dataset?
Audio data in the Group Communication Speech Dialogue Dataset are used to enhance and train speech recognition systems for low-resource languages.
In which projects can the Group Communication Speech Dialogue Dataset be applied?
The Group Communication Speech Dialogue Dataset can be applied in speech recognition system development, language research, and human-computer interaction projects.

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Cite this Work

@dataset{Mobiusi2026,
  title={Group Communication Chinese Voice Dialogue Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/92bbe3bea127b3def4e4cd8f9d06b319?dataset_scene_cate_type=2},
  urldate={2026-02-04},
  keywords={group communication voice data, low-resource language speech recognition, audio datasets},
  version={1.0}
}

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