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Home/Audio/Insurance Negotiation Room Customer Communication Chinese Audio Emotion Analysis Dataset

Insurance Negotiation Room Customer Communication Chinese Audio Emotion Analysis Dataset

#emotion classification #speech emotion recognition #speech-to-text analysis #emotion analysis #customer service interaction #speech recognition #communication strategy optimization

Records: 500 hours
Size: 1.3G
Format: WAV
Provider: MOBIUSI INC
Updated: v1.02026-11-04

AI Analysis & Value Prop

The core advantages of this dataset are: In terms of data quality, it is at the forefront of the industry with up to 99% annotation consistency and completeness. In terms of technological innovation, it adopts advanced emotion annotation methods and data augmentation techniques, significantly improving the accuracy of emotion recognition. Its application value lies in optimizing insurance company customer communication strategies, significantly reducing customer churn due to emotional misjudgments, and increasing customer satisfaction by 20% compared to traditional methods. Compared to similar datasets, it has an exclusive multi-emotion label configuration, which improves accuracy by 15%. Its unique data features and rich emotion labels support a wider range of application scenarios. The dataset design supports multilingual expansion and cross-industry applications, offering high scalability and versatility.

Overview

Dataset Insights

Preview

Sample Examples

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Schema

Technical Specifications

Field	Type	Description
file_name	string	File name
duration	string	Duration
audio_rate	string	Audio sample rate
audio_channel	string	Audio channel
speaker_id	string	A unique identifier for each speaker involved in the conversation.
emotion_label	string	Classify the emotions expressed by the speaker in the audio, such as happiness, anger, sadness, etc.
confidence_score	float	The confidence score of the emotion recognition model for the labeled emotion.
speech_transcript	text	Convert audio content into text for more detailed analysis.
language	string	The type of language used in the audio.
num_speakers	int	The number of speakers participating in the conversation in the audio.
background_noise	string	Describe the type of background noise present in the audio, such as traffic, office, etc.
dialogue_type	string	The type of dialogue in the audio, such as one-on-one, group discussion, etc.

Trust

Compliance Statement

Authorization Type

Proprietary - Commercial AI Training License (No Redistribution)

Commercial Use

Requires exclusive subscription or authorization contract (monthly or per-invocation charging)

Privacy and Anonymization

No PII, no real company names, simulated scenarios follow industry standards

Compliance System

Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

FAQ

Frequently Asked Questions

What is the main purpose of this dataset?: The Insurance Negotiation Room Customer Communication Audio Emotion Analysis Dataset is used to analyze and understand emotional changes in customer communication.

Which industry domain does this dataset belong to?: The dataset belongs to the general daily industry domain.

What is the modality of this dataset?: The modality of this dataset is audio.

What kind of emotion analysis applications can this dataset help build?: This dataset can help build emotion recognition systems to improve emotional understanding in customer communication and enhance service quality.

Who would benefit from this dataset?: Professionals involved in customer service optimization, emotion analysis application development, and customer service training will benefit from this dataset.

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Cite this Work

@dataset{Mobiusi2026,
  title={Insurance Negotiation Room Customer Communication Chinese Audio Emotion Analysis Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/4739b304363736aaecc717211d936848?cate=1},
  urldate={2026-02-04},
  keywords={insurance communication audio dataset, emotion analysis dataset, speech emotion recognition, insurance customer service optimization},
  version={1.0}
}

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