Malayalam Scripted Monologue Speech Dataset for BFSI Domain

The audio dataset comprises scripted monologue speech data in the BFSI domain, featuring native Malayalam speakers from India. It includes speech data, detailed metadata, and accurate transcriptions.

Category

Scripted Prompt Recordings

Total Volume

6000+ prompts

Last updated

July 2025

Number of participants

60+

BFSI scripted monologue speech data for Machine learning in Malayalam (India)

About this Off-the-shelf Speech Dataset

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Introduction

Welcome to the Malayalam Scripted Monologue Speech Dataset tailored for the BFSI (Banking, Financial Services, and Insurance) domain. This dataset empowers the development of advanced Malayalam speech recognition systems, natural language understanding models, and conversational AI solutions focused on the BFSI sector.

Speech Data

This dataset includes over 6,000 scripted prompt recordings in Malayalam, covering a wide range of realistic banking and finance-related scenarios to support robust ASR and voice AI systems.

  • Participant Diversity
  • Speakers: 60 native Malayalam speakers.
  • Regions: Diverse representation from various Kerala provinces to ensure dialect and accent coverage.
  • Demographics: Age range of 18–70, with a male-to-female ratio of 60:40.
  • Recording Details
  • Nature: Scripted monologues and domain-specific prompt recordings.Duration:
  • Audio Format: WAV, mono channel, 16-bit depth, recorded at 8 kHz and 16 kHz sample rates.
  • Environment: Clean, echo-free, and noise-free environments.
  • Topic & Context Diversity

    This dataset spans multiple BFSI-related themes to simulate practical customer interaction scenarios:

  • Customer service interactions
  • Financial transactions & balance inquiries
  • Banking and insurance product queries
  • Loan & credit support
  • Regulatory and compliance questions
  • Technical help and password resets
  • Promotional campaigns and service updates
  • Contextual Elements

    To make the dataset as context-rich as possible, each prompt integrates commonly encountered real-world BFSI elements:

  • Names: Region-specific names in multiple formats
  • Addresses: Local address structures and pronunciations
  • Dates & Times: Typical time expressions used in banking
  • Organization Names: Names of banks, financial firms, and institutions
  • Currencies & Amounts: Spoken currency formats, prices, and numeric data
  • IDs & Transaction Numbers: For authentic service simulation
  • Transcription

    Every audio file is paired with verbatim transcription to streamline ASR and NLP model development.

  • Content: Exact match of each prompt
  • Format: Clean .TXT files, mapped to audio file names
  • Accuracy: Reviewed and validated by native Malayalam linguists
  • Metadata

    Each data point is enriched with detailed metadata for advanced training and analysis:

  • Participant Metadata: Unique ID, age, gender, state, country, dialect
  • Recording Metadata: Transcript, recording setup, sample rate, bit depth, device, file format
  • Applications and Use Cases

    This BFSI-focused dataset is ideal for:

  • Speech Recognition Training: Build or fine-tune ASR models in Malayalam
  • Voice Synthesis Models: Create realistic synthetic banking voices
  • Voice Assistants & IVR: Power smart assistants and bots for finance workflows
  • Chatbot Training: Build virtual agents for financial services
  • NER & Entity Extraction: Train NLP models with real-world financial terms
  • Language Understanding: Improve intent detection, sentiment analysis, and topic modeling
  • Secure & Ethical Data Collection

    All data was collected via FutureBeeAI’s proprietary platform Yugo

  • Entire workflow conducted within a secure, controlled environment
  • Participants gave full consent under strict ethical protocols
  • No PII (Personally Identifiable Information) is included
  • Fully compliant and safe for commercial use
  • License

    This dataset is created and owned by FutureBeeAI and is available for commercial licensing.

    Use Cases

    Use of scripted speech monologues datasets for Automatic Speech Recognition

    ASR

    Use of scripted speech monologues datasets for Conversational AI

    Conversational AI

    Use of scripted speech monologues datasets for Chatbot

    Chatbot

    Use of scripted speech monologues datasets for TTS

    TTS

    Use of scripted speech monologues datasets for Speech analytics

    Speech Analytics

    Use of scripted speech monologues datasets for Mobile speech

    Mobile Speech

    Dataset Sample(s)

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    Dataset Details

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    Language

    Malayalam

    Language code

    ml-in

    Country

    India

    Accents

    Kasaragod, North Malabar ...more

    Gender Distribution

    M:60, F:40

    Age Group

    18-70 Years

    File Details

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    Environment

    Silent

    Bit Depth

    16 bit

    Sample rate

    8KHz & 16KHz

    Channel

    Mono

    Audio file duration

    5 to 30 seconds

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    Prompt 2 Bg