Using Training Data to Improve Speaker Recognition Models

Data is the “fuel”that drives AI system towards continuous progress, but building high-quality datasets isn’t easy. The part where involve data collecting, cleaning, annotating, and privacy protecting are all challenging. Researchers need to collect targeted data to deal with complex problems faced on different fields to make sure the trained models have robustness and generalization capability. Through using rich datasets, AI system can achieve intelligent decision-making in more complex scenario.

Speaker recognition technology is also called voiceprint recognition. It’s a powerful tool that has a wide range of applications in various industries. At its core, speaker recognition is the process of identifying a person based on their voice or speech patterns. This technology has become increasingly popular in recent years, as its applications have expanded and improved.

One of the most common applications of speaker recognition technology is in the field of security. With the help of speaker recognition, security systems can identify and authenticate individuals based on their voice, which can help to prevent unauthorized access to secure areas or data. For example, speaker recognition can be used to grant access to high-security areas in corporate or government buildings.

Speaker recognition technology also has applications in the field of law enforcement, where it can be used to help identify suspects based on their voice. This can be particularly useful in cases where there is no other evidence to link a suspect to a crime.

In addition to security and law enforcement, speaker recognition technology is also being used in the healthcare industry to help identify patients based on their voice. This can be particularly useful in emergency situations where patients are unable to communicate or identify themselves.

Finally, speaker recognition technology is also being used in the field of customer service. With the help of speaker recognition, customer service representatives can identify callers based on their voice and quickly access their account information, making the customer service experience more efficient and personalized.

Nexdata’s Data Solution for Speaker Recognition

1,441 Hours - Italian Speech Data by Mobile Phone

The data were recorded by 3,109 native Italian speakers with authentic Italian accents. The recorded content covers a wide range of categories such as general purpose, interactive, in car commands, home commands, etc. The recorded text is designed by a language expert, and the text is manually proofread with high accuracy. Match mainstream Android, Apple system phones

759 Hours - Hindi Speech Data by Mobile Phone

The data is 759 hours long and was recorded by 1,425 Indian native speakers. The accent is authentic. The recording text is designed by language experts and covers general, interactive, car, home and other categories. The text is manually proofread, and the accuracy is high. Recording devices are mainstream Android phones and iPhones. It can be applied to speech recognition, machine translation, and voiceprint recognition.

1,535 Hours - Mixed Speech with Chinese and English Data by Mobile Phone

The 1,535 Hours - Mixed Speech with Chinese and English Data by Mobile Phone is recorded by 3972 Chinese native speakers with accents covering seven major dialect areas. The recorded text is a mixture of Chinese and English sentences, covering general scenes and human-computer interaction scenes. It is rich in content and accurate in transcription. It can be used for improving the recognition effect of the speech recognition system on Chinese-English mixed reading speech.

357 Hours–Korean Speech Data by Mobile Phone

357 hours of Korean speech data collected by cellphone. It is recorded by 999 Korean in quiet environment and is rich in content. All texts are transtribed by professional annotator. The accuracy rate of sentence is 95%. It can be used for speech recognition, machine translation and voiceprint recognition.

338 Hours-Spanish Speech Data by Mobile Phone

The 338-hour Spanish speech data and is recorded by 800 Spanish-speaking native speakers from Spain, Mexico, Argentina. The recording enviroment is queit. All texts are manually transcribed.The sentence accuracy rate is 95%. It can be applied to speech recognition, machine translation, voiceprint recognition and so on.

In the era of deep integration of data and artificial intelligence, the richness and quality of datasets will directly determine how far an AI technology goes. In the future, the effective use of data will drive innovation and bring more growth and value to all walks of life. With the help of automatic labeling tools, GAN or data augment technology, we can improve the efficiency of data annotation and reduce labor costs.