Revolutionizing In-Vehicle Voice Recognition Technology

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.

Minority languages often face challenges stemming from limited resources, diminished intergenerational transmission, and lack of recognition. This threatens their survival and the cultural diversity they represent. However, modern advancements in technology, particularly in the realm of data resources and speech recognition, are proving to be pivotal tools in safeguarding these languages.

Data resources play a vital role in documenting and studying minority languages. By amassing written texts, audio recordings, and multimedia content, linguists and researchers can build comprehensive linguistic databases. These databases capture the nuances of phonetics, grammar, vocabulary, and cultural context. This wealth of information not only ensures the preservation of these languages but also facilitates their study and analysis.

Speech recognition technology, fueled by machine learning and artificial intelligence, has the potential to bridge language barriers and give a voice to minority languages. Through speech recognition applications, these languages can be transcribed, translated, and shared more widely. This technology not only aids linguists in their research but also enables fluent speakers to engage with and contribute to the preservation process.

Collaboration among various stakeholders is crucial. Governments and organizations should allocate resources for language documentation projects, encouraging the collection and digitization of data resources. Native speakers and local communities are essential in providing linguistic expertise and cultural insights. Linguists and technology experts work hand in hand to develop accurate speech recognition models that can understand and transcribe minority languages effectively.

Moreover, the intersection of data resources and speech recognition goes beyond preservation. It enables the creation of interactive language learning tools and digital platforms. These platforms can offer immersive experiences for learners, helping to bridge the gap between generations and rekindle interest in the language. Speech recognition-powered language apps can facilitate real-time conversations, aiding learners in pronunciation and communication.

Nexdata Minority Language Speech Datasets

120 Hours - Burmese Conversational Speech Data by Mobile Phone

The 120 Hours - Burmese Conversational Speech Data involved more than 130 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 16kHz, 16bit, uncompressed WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

320 Hours - Dari Conversational Speech Data by Telephone

The 320 Hours - Dari Conversational Speech Data collected by telephone involved more than 330 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 8bit, WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

200 Hours - Urdu Conversational Speech Data by Telephone

The 200 Hours - Urdu Conversational Speech Data collected by telephone involved more than 230 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 8bit, WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

200 Hours - Pushtu Conversational Speech Data by Telephone

The 200 Hours - Pushtu Conversational Speech Data collected by telephone involved more than 230 native speakers, developed with proper balance of gender ratio, Speakers would choose a few familiar topics out of the given list and start conversations to ensure dialogues' fluency and naturalness. The recording devices are various mobile phones. The audio format is 8kHz, 8bit, WAV, and all the speech data was recorded in quiet indoor environments. All the speech audio was manually transcribed with text content, the start and end time of each effective sentence, and speaker identification.

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.