.webp&w=3840&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
We Use Cookies!!!
We use cookies to ensure that we give you the best experience on our website. Read cookies policies.
Arabic (Algeria) Scripted Monologue Speech Dataset for Telecom Domain
The audio dataset comprises scripted monologue speech data in the Telecom domain, featuring native Arabic speakers from Algeria. It includes speech data, detailed metadata, and accurate transcriptions.
Category
Scripted Prompt Recordings
Total Volume
6000+ prompts
Last updated
July 2024
Number of participants
60+
Get this Speech Dataset
About this Off-the-shelf Speech Dataset
Introduction
Welcome to the Algerian Arabic Scripted Monologue Speech Dataset for the Telecom Domain. This meticulously curated dataset is designed to advance the development of Arabic language speech recognition models, particularly for the Telecom industry.
Speech Data
This training dataset comprises over 6,000 high-quality scripted prompt recordings in Algerian Arabic. These recordings cover various topics and scenarios relevant to the Telecom domain, designed to build robust and accurate customer service speech technology.
•
Speakers:
60 native Arabic speakers from different regions of Algeria.
•
Regions:
Ensures a balanced representation of Algerian Arabic accents, dialects, and demographics.
•
Participant Profile:
Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.
•
Recording Nature:
Audio recordings of scripted prompts/monologues.
•
Audio Duration:
Average duration of 5 to 30 seconds per recording.
•
Formats:
WAV format with mono channels, a bit depth of 16 bits, and sample rates of 8 kHz and 16 kHz.
•
Environment:
Recordings are conducted in quiet settings without background noise and echo.
•
Topic Diversity:
The dataset encompasses a wide array of topics and conversational scenarios to ensure comprehensive coverage of the Telecom sector. Topics include:
•Customer Service Interactions
•Billing and Payments
•Technical Support
•Product and Service Inquiries
•Network Coverage
•Regulatory and Compliance Queries
•Service Upgrades and Changes
•Domain Specific Statements
•
Other Elements:
To enhance realism and utility, the scripted prompts incorporate various elements commonly encountered in Telecom interactions:
•
Names:
Region-specific names of males and females in various formats.
•
Addresses:
Region-specific addresses in different spoken formats.
•
Dates & Times:
Inclusion of date and time in various telecom contexts, such as billing dates and service activation times.
•
Telecom Terms:
Specific terminology relevant to telecom services, devices, and plans.
•
Numbers & Rates:
Various numbers related to billing amounts, data usage, and call rates.
•
Telecom Providers:
Names of telecom companies, service providers, and related entities.
Each scripted prompt is crafted to reflect real-life scenarios encountered in the Telecom domain, ensuring applicability in training robust natural language processing and speech recognition models.
Transcription Data
In addition to high-quality audio recordings, the dataset includes meticulously prepared text files with verbatim transcriptions of each audio file. These transcriptions are essential for training accurate and robust speech recognition models.
•
Content:
Each text file contains the exact scripted prompt corresponding to its audio file, ensuring consistency.
•
Format:
Transcriptions are provided in plain text (.TXT) format, with files named to match their associated audio files for easy reference.
•
Quality:
All transcriptions are verified for accuracy and consistency by native Arabic transcribers.
Metadata
The dataset provides comprehensive metadata for each audio recording and participant:
•
Participant Metadata:
Unique identifier, age, gender, country, state, and dialect.
•
Other Metadata:
Recording transcript, recording environment, device details, sample rate, bit depth, and file format.
This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Arabic language speech recognition models.
Usage and Applications
This dataset is a versatile resource for various applications within speech recognition, natural language processing, and AI-driven conversational technologies.
•
Speech Recognition Model Training:
High-quality audio recordings and precise transcriptions for training and fine-tuning Arabic speech recognition models.
•
Voice Synthesis:
The diverse and high-quality audio data can train generative AI models for creating synthetic voices.
•
Voice Assistants:
Ideal for training voice assistants tailored to the Telecom domain.
•
Chatbots:
Transcription data can train conversational models, enabling chatbots to respond to customer queries effectively.
•
Entity Recognition:
Sentences include names, dates, currencies, and other domain-specific entities for training NLP models for named entity recognition (NER) tasks.
•
Language Understanding:
Improve language understanding applications like sentiment analysis and topic modeling within the Telecom sector.
Secure and Ethical Collection
•Our proprietary data collection and transcription platform, “Yugo” was used throughout the dataset creation process.
•Data remained within our secure platform, ensuring data security and confidentiality.
•The data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.
•The dataset does not include any personally identifiable information about any participant, making it safe to use.
License
This Algerian Arabic Scripted Monologue Speech Dataset, created by FutureBeeAI, is available for commercial use.
Use Cases
ASR
Conversational AI
Chatbot
Language modelling
TTS
Speech Analytics
Dataset Sample(s)
Samples will be available soon!
Contact us to get the samples immediately for this dataset.
Contact Us
Dataset Demographics
Language
Arabic
Language code
ar-dz
Country
Algeria
Accents
Eastern Hilal,...more
Gender Distribution
M:60, F:40
Age Group
18-70
Audio File Details
Environment
Silent
Bit Depth
16 bit
Sample rate
8KHz & 16KHz
Channel
Mono
Audio file duration
5 to 30 seconds
Start your AI/ML model creation journey with FutureBeeAI!
Contact Us
