[{"@type":"PropertyValue","name":"Format","value":"16kHz, 16 bit, wav, mono channel;"},{"@type":"PropertyValue","name":"Content category","value":"Dialogue based on given topics;"},{"@type":"PropertyValue","name":"Recording condition","value":"Low background noise (indoor);"},{"@type":"PropertyValue","name":"Recording device","value":"Android smartphone, iPhone;"},{"@type":"PropertyValue","name":"Speaker","value":"590 people in total, 46% male and 54% female"},{"@type":"PropertyValue","name":"Country","value":"Portugal(PRT);"},{"@type":"PropertyValue","name":"Language(Region) Code","value":"pt-PT;"},{"@type":"PropertyValue","name":"Language","value":"Portuguese;"},{"@type":"PropertyValue","name":"Features of annotation","value":"Transcription text, timestamp, speaker ID, gender, noise,PII redacted."},{"@type":"PropertyValue","name":"Accuracy Rate","value":"Word Accuracy Rate (WAR) 98%"}]
{"id":1301,"datatype":"1","titleimg":"https://www.nexdata.ai/shujutang/static/image/index/datatang_yuyin_default.webp","type1":"165","type1str":null,"type2":"166","type2str":null,"dataname":"406 Hours European Portuguese Speech Dataset with Spontaneous Dialogues and Transcripts","datazy":[{"title":"Format","desc":"Format","content":"16kHz, 16 bit, wav, mono channel;"},{"title":"Content category","desc":"Content category","content":"Dialogue based on given topics;"},{"title":"Recording condition","desc":"Recording condition","content":"Low background noise (indoor);"},{"title":"Recording device","desc":"Recording device","content":"Android smartphone, iPhone;"},{"title":"Speaker","desc":"Speaker","content":"590 people in total, 46% male and 54% female"},{"title":"Country","desc":"Country","content":"Portugal(PRT);"},{"title":"Language(Region) Code","desc":"Language(Region) Code","content":"pt-PT;"},{"title":"Language","desc":"Language","content":"Portuguese;"},{"title":"Features of annotation","desc":"Features of annotation","content":"Transcription text, timestamp, speaker ID, gender, noise,PII redacted."},{"title":"Accuracy Rate","desc":"Accuracy Rate","content":"Word Accuracy Rate (WAR) 98%"}],"datatag":"Portuguese,European,Mobile Phone","technologydoc":null,"downurl":null,"datainfo":null,"standard":null,"dataylurl":null,"flag":null,"publishtime":null,"createby":null,"createtime":null,"ext1":null,"samplestoreloc":null,"hosturl":null,"datasize":null,"industryPlan":null,"keyInformation":"","samplePresentation":[{"name":"/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-8.wav","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-8.wav?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=o2b3hFQBOItV5vTTX84ngYdKRc0%3D","intro":"Claro que o Gui e o Agar, os nossos outros dois amigos, eh, repararam e foram imediatamente atrás dele.[N]","size":0,"progress":100,"type":"mp3"},{"name":"/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-4.wav","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-4.wav?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=G8agkLSjRAezwmZS39N4MJCAqq4%3D","intro":"No vídeo ele estava a sorrir, estava a fazer palhaçadas, estava atrás da, da Anne.","size":0,"progress":100,"type":"mp3"},{"name":"/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-1.wav","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-1.wav?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=eE0qkg5HGq3dL4711p8CK8gaShI%3D","intro":"Porque eu, lá está, pelo que me disseram, estava tudo bem, tudo bem. Não, não, não houve.[N]","size":0,"progress":100,"type":"mp3"},{"name":"/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-3.wav","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-3.wav?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=R2P2ZIxA4lHfd0YyfVx0duVhBOw%3D","intro":"[OVERLAP/]E é [/OVERLAP]verdade.","size":0,"progress":100,"type":"mp3"},{"name":"/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-2.wav","url":"https://bj-oss-datatang-03.oss-cn-beijing.aliyuncs.com/filesInfoUpload/data/apps/damp/temp/ziptemp/APY231020001_demo1732010406339/APY231020001_demo/0011_001_phone-2.wav?Expires=4102329599&OSSAccessKeyId=LTAI8NWs2pDolLNH&Signature=WbavOJDcMWp%2Fk8GFB%2FyJhQkWz2Q%3D","intro":"Disseram que no início não havia qualquer indício, que ele podia estar triste ou desanimado.[N]","size":0,"progress":100,"type":"mp3"}],"officialSummary":"This dataset contains 406 hours of European Portuguese spontaneous dialogue speech collected through topic-based conversations covering more than 20 domains. Each recording includes accurate transcripts, speaker ID, gender, age, and additional metadata. The dataset was collected from 590 native European Portuguese speakers across diverse regions, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.","dataexampl":null,"datakeyword":["portuguese audio dataset","portuguese speech dataset","portuguese voice dataset","portuguese speech corpus","european portuguese speech dataset","portuguese asr dataset"],"isDelete":null,"ids":null,"idsList":null,"datasetCode":null,"productStatus":null,"tagTypeEn":"Data Type,Language","tagTypeZh":null,"website":null,"samplePresentationList":null,"datazyList":null,"keyInformationList":null,"dataexamplList":null,"bgimg":null,"datazyScriptList":null,"datakeywordListString":null,"sourceShowPage":"speechRec","dataShowType":"[{\"code\":\"0\",\"language\":\"ZH\"},{\"code\":\"1\",\"language\":\"ZH\"},{\"code\":\"2\",\"language\":\"EN,JP,PT,DE,KO,FR,ES\"},{\"code\":\"3\",\"language\":\"EN\"},{\"code\":\"4\",\"language\":\"JP\"}]","productNameEn":"406 Hours - Portuguese(European) Spontaneous Dialogue Smartphone Speech Dataset","BGimg":"brightSpot_audio","voiceBg":["/shujutang/static/image/comm/audio_bg.webp","/shujutang/static/image/comm/audio_bg2.webp","/shujutang/static/image/comm/audio_bg3.webp","/shujutang/static/image/comm/audio_bg4.webp","/shujutang/static/image/comm/audio_bg5.webp"]}
406 Hours European Portuguese Speech Dataset with Spontaneous Dialogues and Transcripts
portuguese audio dataset
portuguese speech dataset
portuguese voice dataset
portuguese speech corpus
european portuguese speech dataset
portuguese asr dataset
This dataset contains 406 hours of European Portuguese spontaneous dialogue speech collected through topic-based conversations covering more than 20 domains. Each recording includes accurate transcripts, speaker ID, gender, age, and additional metadata. The dataset was collected from 590 native European Portuguese speakers across diverse regions, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
What languages and scenarios are covered by Nexdata’s speech recognition datasets?
Nexdata offers speech recognition datasets covering a broad range of languages, dialects, and accents, backed by extensive global language resources. Our datasets include diverse speakers, acoustic environments, and real-world speech scenarios, supporting multilingual ASR, voice assistants, conversational AI, speech-to-text, and other speech-enabled applications.
Can Nexdata customize speech recognition datasets for specific languages or requirements?
Yes. If our off-the-shelf datasets do not fully meet your requirements, Nexdata provides flexible custom data collection, transcription, and annotation services. We can customize datasets based on target languages or dialects, speaker profiles, recording environments, speech scenarios, data volume, and annotation specifications to meet specific ASR development needs.
How does Nexdata ensure the quality and scalability of speech recognition datasets?
Nexdata applies multi-stage quality control throughout speech data collection, transcription, annotation, and validation. Combined with our extensive language resources and scalable collection capabilities, we can support both large-scale multilingual projects and specialized datasets for specific languages, dialects, accents, and speech scenarios.