Filipino (Philippines) Scripted Monologue Speech Dataset for BFSI Domain

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

About this Off-the-shelf Speech Dataset

Introduction

Welcome to the Filipino Scripted Monologue Speech Dataset for the BFSI Domain. This meticulously curated dataset is designed to advance the development of Filipino language speech recognition models, particularly for the BFSI (Banking, Financial Services, and Insurance) industry.

Speech Data

This training dataset comprises over 6,000 high-quality scripted prompt recordings in Filipino. These recordings cover various topics and scenarios relevant to the BFSI domain, designed to build robust and accurate customer service speech technology.

•Participant Diversity:

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Speakers: 60 native Filipino speakers from different regions of Philippines.

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Regions: Ensures a balanced representation of Filipino accents, dialects, and demographics.

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Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

•Recording Details:

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Recording Nature: Audio recordings of scripted prompts/monologues.

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Audio Duration: Average duration of 5 to 30 seconds per recording.

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Formats: WAV format with mono channels, a bit depth of 16 bits, and sample rates of 8 kHz and 16 kHz.

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Environment: Recordings are conducted in quiet settings without background noise and echo.

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Topic Diversity: The dataset encompasses a wide array of topics and conversational scenarios to ensure comprehensive coverage of the BFSI sector. Topics include:

•Customer Service Interactions

•Financial Transactions

•Product and Service Inquiries

•Technical Support

•General Information and Advice

•Regulatory and Compliance Queries

•Domain Specific Statements

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Other Elements: To enhance realism and utility, the scripted prompts incorporate various elements commonly encountered in BFSI interactions:

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Names: Region-specific names of males and females in various formats.

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Addresses: Region-specific addresses in different spoken formats.

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Dates & Times: Inclusion of date and time in various BFSI contexts.

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Organization Names: Names of banks, financial institutions, and other relevant organizations.

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Numbers & Currencies: Various numbers and currencies in domain-specific interactions.

Each scripted prompt is crafted to reflect real-life scenarios encountered in the BFSI 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.

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Content: Each text file contains the exact scripted prompt corresponding to its audio file, ensuring consistency.

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Format: Transcriptions are provided in plain text (.TXT) format, with files named to match their associated audio files for easy reference.

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Quality: All transcriptions are verified for accuracy and consistency by native Filipino transcribers.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

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Participant Metadata: Unique identifier, age, gender, country, state, and dialect.

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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 Filipino 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.

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Speech Recognition Model Training: High-quality audio recordings and precise transcriptions for training and fine-tuning Filipino language speech recognition models.

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Voice Synthesis: The diverse and high-quality audio data can train generative AI models for creating synthetic voices.

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Voice Assistants: Ideal for training voice assistants tailored to the BFSI domain.

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Chatbots: Transcription data can train conversational models, enabling chatbots to respond to customer queries effectively.

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Entity Recognition: Sentences include names, dates, currencies, and other domain-specific entities for training NLP models for named entity recognition (NER) tasks.

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Language Understanding: Improve language understanding applications like sentiment analysis and topic modeling within the BFSI sector.