---
title: "French Speech Recognition Dataset"
description: "This speech recognition dataset comprises 10+ hours of telephone dialogues in French from 20+ native speakers, providing audio recordings with detailed annotations (ID, language, format,…"
url: "https://unidata.pro/datasets/french-speech-recognition-dataset/"
date_modified: "2025-12-11T14:14:55+03:00"
language: "en-US"
---
This speech recognition dataset comprises 10+ hours of telephone dialogues in French from 20+ native speakers, providing audio recordings with detailed annotations (ID, language, format, minutes) to support speech recognition systems, natural language processing, and deep learning models for training and evaluating automatic speech recognition technology

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 10+ — **Text:** Hours
- **Number:** 20+ — **Text:** Speakers

### Tooltips Section

**Tooltip items:**

- **Name:** NLP
- **Name:** LLM
- **Name:** Machine Learning
- **Name:** Audio Processing
- **Name:** ASR
- **Name:** Voice Recognition

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Audio of telephone dialogues in French for training NLP models in real-world conversational scenarios. |
| Data types | Audio |
| Tasks | Speech recognition, NLP |
| Country | France (FRA) |
| Hours of telephone dialogue | 10 |
| Number of speakers | 20 |
| Labeling | Annotation (ID, Language, Format, Minutes) |
| Recording device | Telephone |

**Media Slider:** - **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/02/caucasian-man-35-years-old-holding-a-smartphone.webp)

**Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1P5RhblyzSossaPQO5xez0v66q9AjVnim?usp=sharing)

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Audio Format | WAV, M4A, MP3 |
| Duration | Mean =11 min |
| Recording condition | Low background noise (indoor) |

**Source and data collection methodology:** Source and collection methodology:  Data was collected via crowdsourcing platforms

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Call Centers & Customer Service — **Title:** Improving French Telephone Dialogue Recognition — **Text:** French Telephone Dialogues Dataset provides authentic audio recordings of real conversations. Speech samples covering various accents and natural speech signals help companies train recognition systems for call centers. This supports faster call handling, accurate transcription, and better customer service in industries using automatic speech recognition.
- **Industry:** AI & Machine Learning Research — **Title:** Training Models for French Speech Recognition — **Text:** This dataset serves as reliable training data for machine learning and deep learning models. It consists of high-quality audio files and transcriptions collected from native speakers, allowing researchers to build speech processing systems that achieve high accuracy in transcribing speech and speech translation tasks.
- **Industry:** Multilingual Applications — **Title:** Supporting Natural Language Processing — **Text:** The French audio dataset enhances multilingual speech projects by providing speech samples aligned with other languages. Developers use it for natural language processing, cross-lingual speech translation, and recognition technology in global applications. Its diverse range of audio samples makes it ideal for creating more inclusive and adaptable recognition systems.
- **Industry:** Commercial & Industrial Solutions — **Title:** Deploying Speech Recognition in Real-World Use Cases — **Text:** Businesses can leverage the dataset for commercial usage in areas like call centers, voice assistants, and transcription platforms. Since the database contains audio recordings from different speakers and conditions, companies can integrate recognition technology into commercial use cases with improved accuracy, reliability, and adaptability across multiple speech processing scenarios.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What audio quality and format are provided? — **Answer:** The audio recordings are provided in WAV, M4A, MP3 formats.
- **Question:** What accents and speech variations are represented? — **Answer:** The dataset includes various French accents and dialects, reflecting real-world natural language variations. This diversity improves the performance of recognition technology and learning algorithms when applied to different types of French speech scenarios.
- **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes, you can request a sample of the dataset to test audio quality, transcription accuracy, and metadata coverage. Samples allow developers to confirm the dataset meets their needs for deep learning models and automatic speech recognition systems.
- **Question:** How long does it take to receive the dataset? — **Answer:** After you submit a request, our team reviews the details and prepares the necessary documents. Once the agreement is signed and payment is complete, the dataset will be delivered within 3–10 business days.
- **Question:** Do Unidata datasets follow GDPR and data privacy regulations? — **Answer:** Yes. All Unidata datasets, including the French audio dataset, are curated in compliance with GDPR and other international privacy laws. Data is collected from legally permissible sources to ensure ethical and lawful usage in speech recognition and NLP projects.
- **Question:** How are Unidata datasets stored? — **Answer:** All datasets are securely stored on AWS cloud infrastructure, ensuring high availability and scalability. Storage and management follow ISO 27001 and ISO 27701 standards, providing a secure, privacy-focused environment for speech datasets and audio recordings.
- **Question:** What makes conversational French audio useful for machine learning? — **Answer:** Conversational audio reflects natural human communication, including different speaking styles, sentence structures, and real-world dialogue patterns. This helps machine learning models perform better when processing spontaneous speech rather than only scripted recordings.

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