New Zealand Call Center Speech Dataset for BFSI

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

About this Off-the-shelf Speech Dataset

Introduction

This New Zealand English Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native New Zealand English speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

•Participant Diversity:

•

Speakers: 60 native New Zealand English speakers from our verified contributor pool.

•

Regions: Representing multiple provinces across New Zealand to ensure coverage of various accents and dialects.

•

Participant Profile: Balanced gender mix (60% male, 40% female) with age distribution from 18 to 70 years.

•Recording Details:

•

Conversation Nature: Naturally flowing, unscripted interactions between agents and customers.

•

Call Duration: Ranges from 5 to 15 minutes.

•

Audio Format: Stereo WAV files, 16-bit depth, at 8kHz and 16kHz sample rates.

•

Recording Environment: Captured in clean conditions with no echo or background noise.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

•Inbound Calls:

•Debit Card Block Request

•Transaction Disputes

•Loan Enquiries

•Credit Card Billing Issues

•Account Closure & Claims

•Policy Renewals & Cancellations

•Retirement & Tax Planning

•Investment Risk Queries, and more

•Outbound Calls:

•Loan & Credit Card Offers

•Customer Surveys

•EMI Reminders

•Policy Upgrades

•Insurance Follow-ups

•Investment Opportunity Calls

•Retirement Planning Reviews, and more

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

•Transcription Includes:

•Speaker-Segmented Dialogues

•30 hours-coded Segments

•Non-speech Tags (e.g., pauses, background noise)

•High transcription accuracy with word error rate < 5% due to double-layered quality checks.

These transcriptions are production-ready, making financial domain model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

•

Participant Metadata: ID, age, gender, accent, dialect, and location.

•

Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.

This granularity supports BFSI-specific analytics, sentiment modeling, and dialect-aware ASR training.

Usage and Applications

This dataset is ideal for a range of BFSI voice AI and NLP applications:

•

Automatic Speech Recognition (ASR): Fine-tune English speech-to-text systems for financial interactions.

•

Speech Analytics: Extract customer intent and service patterns in banking and insurance.

•

Voice Assistants & Chatbots: Train secure, domain-aware virtual assistants for BFSI customers.

•

Sentiment Analysis: Analyze customer emotions and satisfaction in financial support calls.

•

Generative AI: Use in building summarization or AI agent response models for BFSI use cases.

Secure and Ethical Collection

•All data was collected using “Yugo,” FutureBeeAI’s proprietary platform under strict ethical and security standards.

•No personally identifiable information is included.

• The Dataset complies with global data privacy guidelines and is copyright-free.

Updates and Customization

We regularly expand this dataset with fresh call recordings and offer tailored options:

•Customization Options:

•

Acoustic Environment: Silent or noisy upon request.

•

Sample Rate: Customizable from 8kHz to 48kHz.

•

Transcription Format: Can follow your QA and formatting requirements.

License

This BFSI domain dataset is commercially licensed and ready for integration into your English ASR, NLP, or conversational AI pipeline.

Use Cases

Call Center Conversational AI

Use of speech data for Automatic Speech Recognition

ASR

Chatbot

Language Modelling

TTS

Speech Analytics

Dataset Sample(s)

Dataset Details

Language

English

Language code

en-nz

Country

New Zealand

Gender Distribution

M:60, F:40

Age Group

18-70 Years

File Details

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

Read the License Terms

Browse FAQs

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New Zealand Call Center Speech Dataset for BFSI

About this Off-the-shelf Speech Dataset

Introduction

Speech Data

Topic Diversity

Transcription

Metadata

Usage and Applications

Secure and Ethical Collection

Updates and Customization

License

Use Cases

Samples will be available soon!

Dataset Details

File Details

British English BFSI CC Speech Data

Spanish (Spain) BFSI CC Speech Data

Indian English BFSI CC Speech Data

Canadian French BFSI CC Speech Data

British English Travel CC Speech Data

American English Delivery & Lgc CC Speech Data

American English Telecom CC Speech Data

Canadian English Retail & E-com CC Speech Data