AI Data Annotation Services: Empowering the Future of Data-driven AI Applications

In the research and application of artificial intelligence, acquiring reliable and rich data has become a crucial part of developing high-efficient algorithm. In order to improve the accuracy and robustness of AI models, enterprises and researchers needs various datasets to train system to cope with complicated scenarios in real applications. This makes the progress of collecting and optimizing data crucial and directly affects the final performance of AI.

Japanese, as one of the most widely used languages in the world, has also become increasingly important in the research and application of speech recognition technology. This article will introduce the current situation and challenges of Japanese speech recognition.

Japanese speech data is an important resource for Japanese speech recognition technology. However, compared with English and Chinese, the amount of available Japanese speech data is relatively small. In addition, the diversity of Japanese dialects and accents poses a significant challenge to speech recognition. This makes it difficult for the machine to recognize the correct pronunciation and intonation of Japanese speech.

Another challenge of Japanese speech recognition is the complexity of the Japanese writing system. Japanese has three scripts, namely hiragana, katakana, and kanji. This makes it more challenging to accurately transcribe spoken Japanese into written text.

Despite these challenges, there have been significant advancements in Japanese speech recognition technology in recent years. One of the most notable achievements is the development of end-to-end speech recognition models. These models use deep learning algorithms to directly convert speech signals into text without the need for intermediate steps, such as phoneme recognition. This has significantly improved the accuracy and speed of Japanese speech recognition.

Another promising development in Japanese speech recognition is the integration of natural language processing (NLP) technology. NLP technology can help the machine better understand the context and meaning of the spoken words, thus improving the accuracy of speech recognition. This technology is particularly important in Japanese, as the language has many homophones that can be difficult for the machine to distinguish without context.

Nexdata Japanese Speech Recognition Data

234 Hours-Japanese Speech Data by Mobile Phone_Reading

It collects 799 Japanese locals and is recorded in quiet indoor places, streets, restaurant. The recording includes 210,000 commonly used written and spoken Japanese sentences. The error rate of text transfer sentence is less than 5%. Recording devices are mainstream Android phones and iPhones.

474 Hours-Japanese Speech Data By Mobile Phone

Recording devices are mainstream Android phones and iPhones.

261 Hours – Japanese Speech Data by Mobile Phone

1006 Japanese native speakers participated in the recording, coming from eastern, western, and Kyushu regions, while the eastern region accounting for the largest proportion. The recording content is rich and all texts have been manually transferred with high accuracy.

500 Hours - Japanese Conversational Speech by Mobile Phone

The 500 Hours - Japanese Conversational Speech of natural conversations collected by phone involved more than 1,000 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. The accuracy rate of sentences is ≥ 95%.

Data quality play a vital role in the development of artificial intelligence. In the future, with the continuous development of AI technology, the collection, cleaning, and annotation of datasets will become more complex and crucial. By continuously improve data quality and enrich data resources, AI systems will accurately satisfy all kinds of needs.