Japanese Speech Dataset 106h Studio Multi-Mic
This Japanese speech dataset offers 106 hours of channel-separated studio conversations across retail, telecom, real estate, finance and medical domains, captured with a 4-channel recording setup. Alongside the primary studio signal, it includes synchronized gooseneck, lavalier and smartphone tracks, plus time-aligned transcripts, fillers, full annotations and metadata. Developed as a multi-mic speech dataset within broader audio datasets, it helps teams train and test models against realistic device and acoustic variation.
This Japanese speech dataset offers 106 hours of channel-separated studio conversations across retail, telecom, real estate, finance and medical domains, captured with a 4-channel recording setup. Alongside the primary studio signal, it includes synchronized gooseneck, lavalier and smartphone tracks, plus time-aligned transcripts, fillers, full annotations and metadata. Developed as a multi-mic speech dataset within broader audio datasets, it helps teams train and test models against realistic device and acoustic variation.
This Japanese speech dataset offers 106 hours of channel-separated studio conversations across retail, telecom, real estate, finance and medical domains, captured with a 4-channel recording setup. Alongside the primary studio signal, it includes synchronized gooseneck, lavalier and smartphone tracks, plus time-aligned transcripts, fillers, full annotations and metadata. Developed as a multi-mic speech dataset within broader audio datasets, it helps teams train and test models against realistic device and acoustic variation.
This Japanese speech dataset offers 106 hours of channel-separated studio conversations across retail, telecom, real estate, finance and medical domains, captured with a 4-channel recording setup. Alongside the primary studio signal, it includes synchronized gooseneck, lavalier and smartphone tracks, plus time-aligned transcripts, fillers, full annotations and metadata. Developed as a multi-mic speech dataset within broader audio datasets, it helps teams train and test models against realistic device and acoustic variation.
Dataset specs
Type
Audio
Sound quality
≥48kHz, ≥32 bit per channel
Region/Locale
ja-JP
Amount
106 hours
Leverage
Improve device-robust speech pipelines with a multi-mic speech dataset that captures acoustic variation across studio, lavalier, gooseneck and smartphone inputs.
Use cases
Develop microphone-robust ASR systems by comparing synchronized channels within this AI training dataset designed for acoustic variability analysis.
Improve device adaptation and transcription quality across microphone types with audio datasets that preserve consistent speech under different pickup conditions.
Train assistant and service workflows that must perform across hardware setups using AI training data drawn from realistic Japanese conversations.
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Dataset specs
Type
Audio
Sound quality
≥48kHz, ≥32 bit per channel
Region/Locale
ja-JP
Amount
106 hours