[Release] Commercially Available Japanese Speaker Diarization Speech Datasets | High-Precision, Large-Scale, Samples Available

The biggest challenge in developing Japanese AI models is 'securing high-quality training data.' In particular, to improve the accuracy of Automatic Speech Recognition (ASR) and Natural Language Processing (NLP), it is essential to have not just reading data, but dialogue data close to real-world environments and data with properly tagged named entities.

To solve these challenges, we introduce three of the latest 2026 self-developed Japanese speech datasets. All are highly accurately annotated, and sample data is available. We hope this will serve as a reference for selecting the optimal dataset for your AI development needs.

### Reproducing Real-World Dialogue: 205-Hour Japanese Speaker Diarization Natural Conversation Dataset **Use Cases**: Speaker diarization models, voice assistants, customer center analysis, natural dialogue models.

**Features and Benefits**: - **Versatility through real-device recording**: Recorded on smartphones, it has acoustic characteristics close to real user environments, including noise cancellation and compression. - **Speaker Diarization/Bidirectional Support**: Since two speakers are recorded on separate tracks, interruptions and overlapping speech are all recorded, capturing the information necessary for dialogue system development. - **Diverse Speaker Attributes**: A total of 234 participants (102 males, 132 females). Ages range from 18 to 60, allowing for the construction of low-bias data. - **High-Precision Annotation**: Character recognition accuracy of over 98%. Timestamps, speaker IDs, and gender information are provided, making it easy to identify speech segments.

### Specialized for Named Entity Recognition: 100-Hour Japanese Entity Reading Dataset **Use Cases**: Voice input forms, NER (Named Entity Recognition), personal information extraction.

This dataset is specialized for 'named entities' (names, addresses, amounts, etc.) where high accuracy is required in speech recognition. Although it is reading data based on scripts, it can be used for training information extraction models because it includes practical entity tags.

**Features and Benefits**: - **Rich Entity Tags**: Important business elements such as personal names, phone numbers, addresses, email addresses, product model numbers, and amounts are tagged (e.g., [PHO], [LOC], [MONEY]). - **Inclusion of Real-World Noise**: Includes environments with 'noise that does not affect recognition' in addition to completely silent environments, contributing to improved model robustness. - **Smartphone Recording**: 16kHz audio quality settings intended for use on actual mobile devices, making it highly compatible with mobile app development. - **Structured Transcription**: Since it is clear where each entity is, not just a transcription, post-processing costs can be significantly reduced.

### High-Quality, Large-Scale: 48kHz 500-Hour Japanese Speaker Diarization Conversation Speech Dataset **Use Cases**: High-precision speech recognition foundation models, R&D, professional services.

A large-scale dataset for developers who want to balance 'quantity' and 'quality.' Recorded in high-quality 48kHz/32bit, it is suitable for training advanced AI models that need to capture fine acoustic features.

**Features and Benefits**: - **Professional Audio Quality**: Adopts high-quality formats of 48kHz sampling and 32bit depth. Ideal for projects requiring delicate acoustic processing such as high-precision speaker recognition. - **Large-Scale Corpus**: With 500 hours of effective time, it contributes significantly to pre-training deep learning models and improving general performance. - **Detailed Annotation Specifications**: Tags such as [IVS] and [PIL] are assigned to inappropriate speech, noise, and private information (phone numbers, etc.), making data cleansing easy.

### Multilingual Speech Datasets Nexdata constantly grasps global research trends and rapidly develops and provides training datasets to accelerate your R&D. Especially for speech datasets, we have a diverse lineup including single/multi-speaker, unsupervised learning corpora, natural conversation, named entities, and domain-specific data, covering over 60 languages and a total of over 1 million hours of data. In addition to new collection and annotation, we can also customize existing datasets.

In addition to Japanese, we possess a wealth of multilingual speaker diarization speech datasets in Asian languages such as English, Korean, and Thai, as well as European languages. Delivery is possible in as little as one week. Please feel free to consult with us.

All datasets are provided under our own copyright, so you can use them with peace of mind. Areas containing personal information (phone numbers, card numbers, etc.) are clearly marked with [PIL] tags, and the audio files are masked, so you can use them without worrying about security risks.

### Nexdata's Initiatives As a world-class AI training data provider, Nexdata has been selling commercially available datasets, collecting data, annotating, and providing data for AI development since 2011, and currently holds approximately 4.5PB of training data. By supplying diverse datasets such as voice, image, video, text, and point clouds to AI companies around the world, we contribute to solving the biggest challenges in the AI industry: 'data quality and quantity.'