Algerian Arabic Call Center Speech Dataset for Real Estate

This Algerian Arabic speech dataset features real-world call center conversations from the Real Estate domain. With detailed metadata and accurate transcriptions, it’s designed to power ASR systems, voice AI, and conversational agents.

Category

Unscripted Call Center Conversations

Total Volume

30 Speech Hours

Last updated

June 2025

Number of participants

60

Arabic (Algeria) Speech to text dataset for Realestate call center

About this Off-the-shelf Speech Dataset

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Introduction

This Algerian Arabic Call Center Speech Dataset for the Real Estate industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Arabic -speaking Real Estate customers. With over 30 hours of unscripted, real-world audio, this dataset captures authentic conversations between customers and real estate agents ideal for building robust ASR models.

Curated by FutureBeeAI, this dataset equips voice AI developers, real estate tech platforms, and NLP researchers with the data needed to create high-accuracy, production-ready models for property-focused use cases.

Speech Data

The dataset features 30 hours of dual-channel call center recordings between native Algerian Arabic speakers. Captured in realistic real estate consultation and support contexts, these conversations span a wide array of property-related topics from inquiries to investment advice offering deep domain coverage for AI model development.

  • Participant Diversity:
  • Speakers: 60 native Algerian Arabic speakers from our verified contributor community.
  • Regions: Representing different provinces across Algeria to ensure accent and dialect variation.
  • Participant Profile: Balanced gender mix (60% male, 40% female) and age range from 18 to 70.
  • Recording Details:
  • Conversation Nature: Naturally flowing, unscripted agent-customer discussions.
  • Call Duration: Average 5–15 minutes per call.
  • Audio Format: Stereo WAV, 16-bit, recorded at 8kHz and 16kHz.
  • Recording Environment: Captured in noise-free and echo-free conditions.
  • Topic Diversity

    This speech corpus includes both inbound and outbound calls, featuring positive, neutral, and negative outcomes across a wide range of real estate scenarios.

  • Inbound Calls:
  • Property Inquiries
  • Rental Availability
  • Renovation Consultation
  • Property Features & Amenities
  • Investment Property Evaluation
  • Ownership History & Legal Info, and more
  • Outbound Calls:
  • New Listing Notifications
  • Post-Purchase Follow-ups
  • Property Recommendations
  • Value Updates
  • Customer Satisfaction Surveys, and others
  • Such domain-rich variety ensures model generalization across common real estate support conversations.

    Transcription

    All recordings are accompanied by precise, manually verified transcriptions in JSON format.

  • Transcription Includes:
  • Speaker-Segmented Dialogues
  • Time-coded Segments
  • Non-speech Tags (e.g., background noise, pauses)
  • High transcription accuracy with word error rate below 5% via dual-layer human review.
  • These transcriptions streamline ASR and NLP development for Arabic real estate voice applications.

    Metadata

    Detailed metadata accompanies each participant and conversation:

  • Participant Metadata: ID, age, gender, location, accent, and dialect.
  • Conversation Metadata: Topic, call type, sentiment, sample rate, and technical details.
  • This enables smart filtering, dialect-focused model training, and structured dataset exploration.

    Usage and Applications

    This dataset is ideal for voice AI and NLP systems built for the real estate sector:

  • Automatic Speech Recognition (ASR): Train high-accuracy speech-to-text models in Algerian Arabic.
  • Speech Analytics: Extract insights on buyer interest, investment intent, and property preferences.
  • Chatbots & Voice Assistants: Develop smart real estate virtual agents.
  • Sentiment Analysis: Detect urgency, uncertainty, or interest in property-related calls.
  • Generative AI: Fine-tune Arabic language models for summarizing or responding to property inquiries.
  • Secure and Ethical Collection

  • Data collected via FutureBeeAI’s secure platform “Yugo” with strict ethical oversight.
  • No personally identifiable information is included.
  • Fully compliant with global data privacy standards and copyright-free.
  • Updates and Customization

    We continuously enhance this dataset with new recordings and offer full customization:

  • Customization Options:
  • Environment: Silent, noisy, or varied real-world conditions on request.
  • Sample Rate: Adjustable from 8kHz to 48kHz.
  • Transcription: Custom formats and QA guidelines available.
  • License

    This Real Estate domain dataset is commercially licensed and ready for use in your Arabic ASR, NLP, and voice AI workflows.

    Use Cases

    Use of speech data in Conversational AI

    Call Center Conversational AI

    Use of speech data for Automatic Speech Recognition

    ASR

    Use of speech data for Chatbot & voicebot creation

    Chatbot

    Use of speech data in Language Modeling

    Language Modelling

    Use of speech data in Text-into-speech

    TTS

    Speech data usecase in Speech Analytics

    Speech Analytics

    Dataset Sample(s)

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    Dataset Details

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    Language

    Arabic

    Language code

    ar-dz

    Country

    Algeria

    Accents

    Eastern Hilal, Central Hilal ...more

    Gender Distribution

    M:60, F:40

    Age Group

    18-70 Years

    File Details

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    Environment

    Silent, Noisy

    Bit Depth

    16 bit

    Format

    wav

    Sample rate

    8khz & 16khz

    Channel

    Stereo (dual-channel, separated speakers)

    Audio file duration

    5-15 minutes

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