The Challenges of Children Speech Recognition

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 made tremendous strides in recent years, offering convenience and accessibility to users across various industries. However, when it comes to recognizing the speech of children, the technology faces a unique set of challenges. In this article, we will explore the complexities involved in children's speech recognition and the efforts being made to address these challenges.

Diverse Speech Patterns

Children's speech evolves significantly as they grow and develop. Infants and toddlers have different speech patterns and articulation compared to older children and adults. These differences can include pitch, tone, pronunciation, and vocabulary. As a result, developing speech recognition systems that can adapt to the ever-changing speech of children is a formidable challenge.

Limited Data Availability

Speech recognition technology relies heavily on vast datasets for training. However, there is a scarcity of comprehensive speech datasets for children in various age groups. This lack of data presents a significant hurdle for developing accurate recognition models. Additionally, collecting and transcribing children's speech data is more time-consuming and challenging compared to adult speech data.

Vocabulary and Language Variability

Children often use words and phrases that are specific to their age and stage of development. This variability in vocabulary and language usage poses a challenge for speech recognition systems. The technology must be equipped to understand and adapt to the age-appropriate terms and phrases that children use, which can differ significantly from adult language.

Background Noise and Environmental Factors

Children are often in environments with high levels of background noise, whether it's in a classroom, playground, or even their own homes. Recognizing speech amidst such noise is more challenging, and existing speech recognition models may struggle to filter out irrelevant sounds and focus on the child's speech.

Lack of Context and Disfluencies

Children's speech is often characterized by disfluencies, such as repetitions, hesitations, and corrections. Recognizing and interpreting these disfluencies is essential for accurate speech recognition. Without understanding the context, the technology may misinterpret these disfluencies as errors, leading to inaccuracies in transcriptions.

Ethical and Privacy Considerations

Children's speech recognition raises ethical and privacy concerns. Collecting, storing, and processing data from minors must be done with the utmost care, taking into account privacy regulations and the need to protect sensitive information. Striking the right balance between technology advancement and privacy is a crucial challenge.

Nexdata Children Speech Data

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.

299 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.

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.

Standing at the forefront of technology revolution, we are well aware of the power of data. In the future, through contentiously improve data collection and annotation process, AI system will become more intelligent. All walks of life should actively embrace the innovation of data-driven to stay ahead in the fierce market competition and bring more value for society.