Improving Speech Recognition through Children Speech Data

With the widespread machine learning technology, data’s importance shown. Datasets isn’t just provide the foundation for the architecture of AI system, but also determine the breadth and depth of applications. From anti-spoofing to facial recognition, to autonomous driving, perceived data collection and processing have become a prerequisites for achieving technological breakthroughs. Hence, high-quality data sources are becoming an important asset for market competitiveness.

Speech recognition technology has become increasingly prevalent in our daily lives, offering convenience and efficiency in various applications. However, one area that poses unique challenges is recognizing and understanding children's speech. The utilization of children speech data has proven to be invaluable in enhancing speech recognition technology, making it more accurate and effective for younger users.

Children's speech differs significantly from adult speech due to factors such as their developing vocal apparatus, limited vocabulary, and pronunciation variations. Collecting and analyzing large volumes of children speech data has enabled researchers and technologists to train speech recognition systems specifically tailored for young users. This specialized training allows these systems to better interpret and understand children's speech patterns and nuances.

The availability of comprehensive children speech data has led to notable advancements in speech recognition technology. By training algorithms on diverse datasets, these systems can adapt to various age groups and capture the specific characteristics of children's speech, including intonation, pronunciation, and language developmental stages. This has resulted in improved accuracy and performance when it comes to recognizing and transcribing children's speech.

Additionally, the use of children speech data has benefited educational applications and language learning tools. By incorporating speech recognition technology into these resources, children can receive real-time feedback on their pronunciation and language skills. This interactive approach fosters engagement and provides personalized learning experiences, ultimately enhancing language acquisition for young learners.

Furthermore, children speech data plays a crucial role in assisting children with speech and language disorders. Speech recognition systems trained on diverse children speech data can support speech therapy by accurately analyzing and providing feedback on a child's speech patterns. This aids in early detection and intervention, promoting effective and targeted therapy strategies.

Nexdata Children Speech Data

200 Hours - American Children Speech Data By Mobile Phone

The data is recorded by 290 children from the U.S.A, with a balanced male-female ratio. The recorded content of the data mainly comes from children's books and textbooks, which are in line with children's language usage habits. The recording environment is relatively quiet indoors, the text is manually transferred with high accuracy.

393 Hours - Korean Children Speech Data by Mobile Phone

Mobile phone captured audio data of Korean children, with total duration of 393 hours. 1085 speakers are children aged 6 to 15; the recorded text contains common children's languages such as essay stories, and numbers. All sentences are manually transferred with high accuracy.

55 Hours - British Children Speech Data by Microphone

It collects 201 British children. The recordings are mainly children textbooks, storybooks. The average sentence length is 4.68 words and the average sentence repetition rate is 6.6 times. This data is recorded by high fidelity microphone. The text is manually transcribed with high accuracy.

50 Hours - American Children Speech Data by Microphone

It is recorded by 219 American children native speakers. The recording texts are mainly storybook, children's song, spoken expressions, etc. 350 sentences for each speaker. Each sentence contain 4.5 words in average. Each sentence is repeated 2.1 times in average. The recording device is hi-fi Blueyeti microphone. The texts are manually transcribed.

Data-driven AI transformation is deeply affecting our ways of life and working methods. The dynamic nature of data is the key for artificial intelligent models to maintain high performance. Through constantly collecting new data and expanding the existing ones, we can help models better cope with new problems. If you have data requirements, please contact Nexdata.ai at [email protected].