Large-Scale Voice Recognition Data for Advanced Models

In the forefront of artificial intelligence, large models, such as those based on deep learning architectures, require vast amounts of data to achieve exceptional performance. Our Large-Scale Voice Recognition Data is meticulously curated to provide the extensive and diverse dataset necessary for training these sophisticated models, enabling breakthroughs in voice recognition technology.

Key Features

Massive Data Volume: Our database contains billions of voice samples and millions of hours data, ensuring ample data to train and fine-tune large models for optimal accuracy.

Wide-Ranging Diversity: Includes voice samples from speakers of various ages, genders, ethnicities, and accents, covering hundreds of languages and dialects to support global applications.

High-Fidelity Recordings: Each sample is recorded in high-quality environments, ensuring clear and precise audio that enhances model training.

Varied Contexts: The data encompasses a broad spectrum of contexts, including conversational speech, commands, narrative passages, and spontaneous dialogue.

Detailed Annotations: Comprehensive metadata and annotations accompany each sample, including phonetic transcriptions, timestamps, speaker demographics, and environmental context.

How it Works

Algorithm

Development

Data Demand

Generation

Dataset

Definition/Design

Trial and

Improvement

Mass

production

Quality Control

Data Package

Delivery

Data Collection and Annotation

We have all kinds of audio classification dataset,for example speech command dataset, common voice dataset.Beside this there is North American voice dataset,African sound dataset，Asian audio datasets，European voices dataset.

Environments

Indoor

Studio

Outdoor

Incar

Devices

Mobile (iOS/Android)

Computer (Desktop/Laptop)

Pro (Hi-Fi recorder/Mic Array)

Speakers

Language: Chinese/English/French/German…

Gender balanced: 1:1

Age: Children/Senior

Education Background

Machine

annotate

Human

transcribe / Validate

Rounds QA by

human & machine

Voice Dataset Annotation

Accuracy between 95%~98%

Surfing Tech applies its own algorithm during speech dataset annotation to ensure high efficiency and accuracy. We achieve above 95% accuracy rate after three rounds of quality inspection, which makes the audio datasets more valuable for speech emotion recognition dataset, semantic understanding, and human-computer interaction.

Speech Data Portfolio

Speech Dataset

Over 30,000 hours of voice dataset collection

Multi-region audio datasets： Australia, North America, South America, Europe, Asia

Speech Data Age Range

Children mandarin speech emotion dataset 3-12 years

Adult audio dataset Senior Mandarin: 800 speakers

Accent

We have speech emotion recognition dataset in various regional dialects

Such as Hakka, English dialects,Hindi voice data, etc.

Central China: 1,000 speakers

Audio Dataset Environment

Indoor voice dataset,Customer service voice dataset

Work environment speech dataset

Language

English voice dataest: North American, Australian, Singapores

Multilingual voice data:Swahili speech data

Russian speech dataest，French voices dataset

Singaporean English,Kazakh speech dataests