Spanish Speech Dataset for recognition task

Dataset comprises 488 hours of telephone dialogues in Spanish, collected from 600 native speakers across various topics and domains. This dataset boasts an impressive 98% word accuracy rate, making it a valuable resource for advancing speech recognition technology.

By utilizing this dataset, researchers and developers can advance their understanding and capabilities in automatic speech recognition (ASR) systems, transcribing audio, and natural language processing (NLP). - Get the data

The dataset includes high-quality audio recordings with text transcriptions, making it ideal for training and evaluating speech recognition models.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fa3f375fb273dcad3fe17403bdfccb63b%2Fssssssssss.PNG?generation=1739884059328284&alt=media" alt=""> - Audio files: High-quality recordings in WAV format - Text transcriptions: Accurate and detailed transcripts for each audio segment - Speaker information: Metadata on native speakers, including gender and etc - Topics: Diverse domains such as general conversations, business and etc

This dataset is a valuable resource for researchers and developers working on speech recognition, language models, and speech technology.

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The BengaliSpeechRecognitionDataset (BSRD) is a comprehensive dataset designed for the development and evaluation of Bengali speech recognition and text-to-speech systems. This dataset includes a collection of Bengali characters and their corresponding audio files, which are generated using speech synthesis models. It serves as an essential resource for researchers and developers working on automatic speech recognition (ASR) and text-to-speech (TTS) applications for the Bengali language. Key Features: • Bengali Characters: The dataset contains a wide range of Bengali characters, including consonants, vowels, and unique symbols used in the Bengali script. This includes standard characters such as 'ক', 'খ', 'গ', and many more. • Corresponding Speech Data: For each Bengali character, an MP3 audio file is provided, which contains the correct pronunciation of that character. This audio is generated by a Bengali text-to-speech model, ensuring clear and accurate pronunciation. • 1000 Audio Samples per Folder: Each character is associated with at least 1000 MP3 files. These multiple samples provide variations of the character's pronunciation, which is essential for training robust speech recognition systems. • Language and Phonetic Diversity: The dataset offers a phonetic diversity of Bengali sounds, covering different tones and pronunciations commonly found in spoken Bengali. This ensures that the dataset can be used for training models capable of recognizing diverse speech patterns. • Use Cases: o Automatic Speech Recognition (ASR): BSRD is ideal for training ASR systems, as it provides accurate audio samples linked to specific Bengali characters. o Text-to-Speech (TTS): Researchers can use this dataset to fine-tune TTS systems for generating natural Bengali speech from text. o Phonetic Analysis: The dataset can be used for phonetic analysis and developing models that study the linguistic features of Bengali pronunciation. • Applications: o Voice Assistants: The dataset can be used to build and train voice recognition systems and personal assistants that understand Bengali. o Speech-to-Text Systems: BSRD can aid in developing accurate transcription systems for Bengali audio content. o Language Learning Tools: The dataset can help in creating educational tools aimed at teaching Bengali pronunciation.

…………………………………..Note for Researchers Using the dataset………………………………………………………………………

This dataset was created by Shuvo Kumar Basak. If you use this dataset for your research or academic purposes, please ensure to cite this dataset appropriately. If you have published your research using this dataset, please share a link to your paper. Good Luck.

Discover our Arabic Speech Dataset with 10+ hours of UAE dialogues in M4A/MP3/WAV/AAC. Clean, annotated audio for ASR training

German Telephone Dialogues Dataset - 431 Hours

Dataset comprises 431 hours of high-quality audio recordings from 590+ native German speakers, featuring telephone dialogues across diverse topics and domains. With a 95% sentence accuracy rate, this essential dataset is ideal for training and evaluating German speech recognition systems. - Get the data

  Dataset characteristics:

Characteristic Data

Description Audio of telephone dialogues in German for training… See the full description on the dataset page: https://huggingface.co/datasets/ud-nlp/german-speech-recognition-dataset.

Unidata’s Italian Speech Recognition dataset refines AI models for better speech-to-text conversion and language comprehension

Arabic Speech Commands Dataset

This dataset is designed to help train simple machine learning models that serve educational and research purposes in the speech recognition domain, mainly for keyword spotting tasks.

Dataset Description

Our dataset is a list of pairs (x, y), where x is the input speech signal, and y is the corresponding keyword. The final dataset consists of 12000 such pairs, comprising 40 keywords. Each audio file is one-second in length sampled at 16 kHz. We have 30 participants, each of them recorded 10 utterances for each keyword. Therefore, we have 300 audio files for each keyword in total (30 * 10 * 40 = 12000), and the total size of all the recorded keywords is ~384 MB. The dataset also contains several background noise recordings we obtained from various natural sources of noise. We saved these audio files in a separate folder with the name background_noise and a total size of ~49 MB.

Dataset Structure

There are 40 folders, each of which represents one keyword and contains 300 files. The first eight digits of each file name identify the contributor, while the last two digits identify the round number. For example, the file path rotate/00000021_NO_06.wav indicates that the contributor with the ID 00000021 pronounced the keyword rotate for the 6^th time.

Data Split

We recommend using the provided CSV files in your experiments. We kept 60% of the dataset for training, 20% for validation, and the remaining 20% for testing. In our split method, we guarantee that all recordings of a certain contributor are within the same subset.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. For more details, see the LICENSE file in this folder.

Citations

If you want to use the Arabic Speech Commands dataset in your work, please cite it as:

@article{arabicspeechcommandsv1,
   author = {Ghandoura, Abdulkader and Hjabo, Farouk and Al Dakkak, Oumayma},
   title = {Building and Benchmarking an Arabic Speech Commands Dataset for Small-Footprint Keyword Spotting},
   journal = {Engineering Applications of Artificial Intelligence},
   year = {2021},
   publisher={Elsevier}
}

Explore our Slovenian Speech Dataset with 10+ hours of clean phone dialogues in MP3/WAV, fully annotated for ASR and language models

French Speech Dataset for recognition task

Dataset comprises 547 hours of telephone dialogues in French, collected from 964 native speakers across various topics and domains, with an impressive 98% Word Accuracy Rate. It is designed for research in speech recognition, focusing on various recognition models, primarily aimed at meeting the requirements for automatic speech recognition (ASR) systems.

By utilizing this dataset, researchers and developers can advance their understanding and capabilities in natural language processing (NLP), speech recognition, and machine learning technologies. - Get the data

The dataset includes high-quality audio recordings with accurate transcriptions, making it ideal for training and evaluating speech recognition models.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fb7af35fb0b3dabe083683bebd27fc5e5%2Fweweewew.PNG?generation=1739885543448162&alt=media" alt="">

• Audio files: High-quality recordings in WAV format
• Text transcriptions: Accurate and detailed transcripts for each audio segment
• Speaker information: Metadata on native speakers, including gender and etc
• Topics: Diverse domains such as general conversations, business and etc

The native speakers and various topics and domains covered in the dataset make it an ideal resource for research community, allowing researchers to study spoken languages, dialects, and language patterns.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

The Speech Commands Dataset is a dataset of 30,000 short (1-3 seconds) audio recordings of 30 different spoken words. It is a popular dataset for keyword spotting and speech recognition research. The dataset is split into a training set of 24,000 recordings and a test set of 6,000 recordings.

This dataset is designed for building and improving speech recognition systems. It features transcribed customer service calls from real interactions across 160+ industries, including retail, banking, telecom, logistics, healthcare, and entertainment. Calls are natural, unscripted, and emotion-rich — making the data especially valuable for training models that must interpret speech under real-world conditions.

Each dataset entry includes:

• Full call transcription (agent + customer dialogue)
• Human-written call summary
• Overall sentiment label: positive, neutral, or negative
• Metadata: call duration, caller location (city, state, country), timestamp
• Optional: company name and industry tag

Use this dataset to:

• Train speech-to-text models on real customer language patterns. -Benchmark or evaluate speech recognition tools in support settings
• Improve voice interfaces, chatbots, and IVR systems.
• Model tone, frustration cues, and escalation behaviors
• Support LLM fine-tuning for tasks involving spoken input.s

This dataset provides your speech recognition models with exposure to genuine customer conversations, helping you build tools that can listen, understand, and act in line with how people actually speak.

The larger the volume you purchase, the lower the price will be.

The global Artificial Intelligence (AI) Training Dataset market is experiencing robust growth, driven by the increasing adoption of AI across diverse sectors. The market's expansion is fueled by the burgeoning need for high-quality data to train sophisticated AI algorithms capable of powering applications like smart campuses, autonomous vehicles, and personalized healthcare solutions. The demand for diverse dataset types, including image classification, voice recognition, natural language processing, and object detection datasets, is a key factor contributing to market growth. While the exact market size in 2025 is unavailable, considering a conservative estimate of a $10 billion market in 2025 based on the growth trend and reported market sizes of related industries, and a projected CAGR (Compound Annual Growth Rate) of 25%, the market is poised for significant expansion in the coming years. Key players in this space are leveraging technological advancements and strategic partnerships to enhance data quality and expand their service offerings. Furthermore, the increasing availability of cloud-based data annotation and processing tools is further streamlining operations and making AI training datasets more accessible to businesses of all sizes. Growth is expected to be particularly strong in regions with burgeoning technological advancements and substantial digital infrastructure, such as North America and Asia Pacific. However, challenges such as data privacy concerns, the high cost of data annotation, and the scarcity of skilled professionals capable of handling complex datasets remain obstacles to broader market penetration. The ongoing evolution of AI technologies and the expanding applications of AI across multiple sectors will continue to shape the demand for AI training datasets, pushing this market toward higher growth trajectories in the coming years. The diversity of applications—from smart homes and medical diagnoses to advanced robotics and autonomous driving—creates significant opportunities for companies specializing in this market. Maintaining data quality, security, and ethical considerations will be crucial for future market leadership.

Spanish Telephone Dialogues Dataset - 488 Hours

Dataset comprises 488 hours of high-quality telephone audio recordings in Spanish, featuring 600 native speakers and achieving a 95% sentence accuracy rate. Designed for advancing speech recognition models and language processing, this extensive speech data corpus covers diverse topics and domains, making it ideal for training robust automatic speech recognition (ASR) systems. - Get the data

  Dataset characteristics:… See the full description on the dataset page: https://huggingface.co/datasets/ud-nlp/spanish-speech-recognition-dataset.

American Telephone Dialogues Dataset - 1,136 Hours

The dataset includes 1,136 hours of annotated telephone dialogues from 1,416 native speakers across the United States. Designed for advancing speech recognition models and language processing, this extensive speech data corpus covers diverse topics and domains, making it ideal for training robust automatic speech recognition (ASR) systems. - Get the data

  Dataset characteristics:

Characteristic Data… See the full description on the dataset page: https://huggingface.co/datasets/ud-nlp/american-speech-recognition-dataset.

Discover the Chichewa Customer Speech Dataset, perfect for AI training, language processing, and speech analysis to develop advanced communication systems.

High-quality general conversation speech datasets in English for news applications. Ideal for AI training, speech recognition, and NLP models.

French Telephone Dialogues Dataset - 547 Hours

his speech recognition dataset comprises 547 hours of telephone dialogues in French from 964 native speakers, providing audio recordings with detailed annotations (text, speaker ID, gender, age) to support speech recognition systems, natural language processing, and deep learning models for training and evaluating automatic speech recognition technology. - Get the data

  Dataset characteristics:

Characteristic Data… See the full description on the dataset page: https://huggingface.co/datasets/ud-nlp/french-speech-recognition-dataset.

This dataset contains two new benchmark corpora designed for low-resource languages spoken in the Democratic Republic of the Congo: The Lingala Read Speech Corpus LRSC, with 4.3 hours of labelled audio, and the Congolese Speech Radio Corpus CSRC, which offers 741 hours of unlabeled audio spanning four significant low-resource languages of the region (Lingala, Tshiluba, Kikongo and Congolese Swahili). Collecting speech and audio for this dataset involved two sets of processes: (1) for LRSC, 32 Congolese adult participants were instructed to sit in a relaxed manner within centimetres of an audio recording device or smartphone and read from the text utterances; (2) for CSRC, recording from the archives of a broadcast station were pre-processed and curated. Congolese languages tend to fall into the “low-resource” category, which, in contrast to “high-resource” languages, has fewer datasets accessible, limiting the development of Conversational Artificial Intelligence. This results in creating the speech recognition datasets for low-resource Congolese languages. The proposed dataset contains two sections. The first section involves training a supervised speech recognition module, while the second involves pre-training a self-supervised model. Both sections feature a wide variety of speech and audio taken in various environments, with the first section featuring a speech having its corresponding transcription and the second featuring a collection of pre-processed raw audio data.

This online repository contains the speech recognition model sets and the recording transcripts used in the phoneme/syllable recognition experiments reported in [1].Speech recognition model sets-----------------------------------------The speech recognition model sets are available as a tarball,named model.tar.gz, in this repository.The models were trained on Cantonese and English data. For each language, two model sets were trained according to the background setting and the mixed-condition setting respectively. All models are DNN-HMM models, which are hybrid feed-forward neural network models with 6 hidden layers and 2048 neurons per layer. Details can be found in [1]. The Cantonese models include a bigram syllable language model. The English models include a bigram phoneme language model. All model sets are provided in the kaldi format.1. The background-cantonese model was trained on CUSENT (68 speakers, 19.4 hours) of read Cantonese speech.2. The background-english model was trained on WSJ-SI84 (83 speakers, 15.2 hours) of read English speech3. The mixed-condition-cantonese model was trained on background-cantonese data and ShefCE Cantonese training data (25 speakers, 9.7 hours).4. The mixed-condition-english model was trained on background-english data and ShefCE English training data (25 speakers, 2.3 hours)Recording transcripts----------------------------The recording transcripts are available as a tarball, named, stms.tar.gz, in this repository. These transcripts cover the ShefCE portion of the training data and the ShefCE test data.Four files can be found in the stms.tar.gz archive. - ShefCE_RC.train.v*.stm contains the transcripts for ShefCE training set (Cantonese)- ShefCE_RE.train.v*.stm contains the transcripts for ShefCE training set (English)- ShefCE_RC.test.v*.stm contains the transcripts for ShefCE test set (Cantonese)- ShefCE_RE.test.v*.stm contains the transcripts for ShefCE test set (English)The ShefCE corpus data can be accessed online with DOI:10.15131/shef.data.4522907Please cite [1] for the use of ShefCE data, models or transcripts.[1] Raymond W. M. Ng, Alvin C.M. Kwan, Tan Lee and Thomas Hain, "ShefCE: A Cantonese-English Bilingual Speech Corpus for Pronunciation Assessment", in Proc. The 42th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017.

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