Restaurant Onsite Call and Reminder Chinese Speech Recognition Audio Dataset

Name: Restaurant Onsite Call and Reminder Chinese Speech Recognition Audio Dataset
Creator: MOBIUSI INC
Published: 2026-02-04

#Speech recognition #natural language processing #speech-to-text #Intelligent customer service #voice assistants #automatic speech recognition

500 hours
1.6G
WAV
CC-BY-NC-SA 4.0
MOBIUSI INC

Updated:2026-02-04

AI Analysis & Value Prop

In the retail e-commerce industry, with the increasing demand for personalized services and intelligent interaction, improving user experience through speech recognition technology has become a trend. However, existing speech recognition systems perform poorly in noisy restaurant environments, with low accuracy and inability to effectively recognize multiple languages or dialects. This dataset aims to address the challenges of speech recognition in complex dining environments, helping to improve the accuracy of automated systems. Data collection uses professional recording equipment, selecting restaurant scenarios at different times and conditions to ensure coverage of diverse noise backgrounds. Quality control includes multi-round manual annotation and consistency checks, with a team composed of trained language experts and data analysts. Preprocessing steps include noise reduction, audio segmentation, volume leveling, etc., with data categorized by scene and phoneme type stored in WAV format.

Dataset Insights

Sample Examples

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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_gender	string	The gender of the speaker in the recording.
speaker_age_group	string	The age group of the speaker in the recording, e.g., child, teenager, adult, senior.
accent	string	The accent of the speaker in the recording, e.g., Standard Mandarin, Cantonese, Beijing Accent, etc.
speech_emotion	string	The emotional state expressed by the speaker in the recording, e.g., happy, sad, angry, etc.
background_noise_level	string	The level of background noise in the recording such as none, slight, or significant noise.
speech_rate	float	The rate of speech by the speaker, expressed in words per minute (WPM).
clarity	string	The clarity of the speech in the recording, e.g., very clear, clear, unclear.

Compliance Statement

Authorization Type	CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
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

Frequently Asked Questions

What application scenarios is this dataset suitable for?: This dataset is suitable for developing speech recognition applications in the healthcare field, such as automatic reminder systems and emergency call systems.

Does this dataset include audio in different languages or accents?: Whether the dataset includes audio in different languages or accents requires checking the dataset's detailed description.

How can this dataset be used to improve the performance of a speech recognition system?: The diverse speech samples in this dataset can be used to train models to improve recognition accuracy and robustness.

What healthcare scenarios are covered by the audio samples in this dataset?: The audio samples cover common call and reminder scenarios such as medication reminders and health check notifications.

When using this dataset for research, do privacy issues need to be considered?: Yes, researchers must adhere to relevant privacy protection and data compliance requirements during use.

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Cite this Work

@dataset{Mobiusi2026,
  title={Restaurant Onsite Call and Reminder Chinese Speech Recognition Audio Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/9c0f01ef821d67cd860cd4b329d0e87a?dataset_scene_cate_type=4},
  urldate={2026-02-04},
  keywords={Chinese speech recognition, retail e-commerce speech data, restaurant speech recognition},
  version={1.0}
}

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