In 2021, French was the first language spoken by over 71 percent of the population of Montréal, Québec in Canada. 20.4 percent of the city's residents had English as their first language, 6.7 percent used both English and French as their primary language, and 1.6 percent of the population spoke another language. That same year, 46.4 percent of people living in the province of Québec could speak both English and French.

Over the past fifty years, the proportion of Quebecers speaking both English and French has increased steadily, from **** percent in 1971 to almost half the population (**** percent) in 2021. The rate of English-French bilingualism, on the other hand, has declined in the rest of the country: outside Quebec, just over ten percent of people were bilingual in English and French in 2001, compared to *** percent two decades later.

Data on the knowledge of official languages by the population of Canada and Canada outside Quebec, and of all provinces and territories, for Census years 1951 to 2021.

Data on type and level of French program attended, number of years of primary or secondary schooling in a regular French program in a French-language school and mother tongue for the population outside of Quebec, in private households in Canada outside of Quebec, provinces and territories, census divisions and census subdivisions.

The statistic reflects the distribution of languages in Canada in 2022. In 2022, 87.1 percent of the total population in Canada spoke English as their native tongue.

Data on the first official language spoken of the population of Canada and Canada outside Quebec, and of all provinces and territories, for Census years 1971 to 2016.

Introduction

Welcome to the Canadian French General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of French speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Canadian French communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade French speech models that understand and respond to authentic Canadian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Canadian French. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple French speech and language AI applications:

First official language spoken by immigrant status and period of immigration for the population of Canada and Canada outside Quebec, and of all provinces and territories, for Census years 1971 to 2021.

In 2021, most of the population of the city of Montreal, located in the Canadian province of Quebec, could speak both English and French. In fact, approximately 1.23 million men and 1.68 million women were bilingual. Of those who spoke only one of the official languages, the majority (1.43 million people) spoke only French. In addition, more than 68,400 people did not know either language, with women outnumbering men.

Data on English spoken at home by French spoken at home, Indigenous language spoken at home, other non-official language spoken at home, mother tongue and gender for the population excluding institutional residents for Canada, provinces and territories, census divisions and census subdivisions.

Introduction

This Canadian French Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for French-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Canadian French speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

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

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

This dataset displays information regarding the language spoken most often at home. This data is available on the Census Division level, and is available from the 2006 Canadian Census. This data was obtained through: Statistics Canada. This data refers to the language spoken most often at home by the individual at the time of the census. Other languages spoken at home on a regular basis were also collected. Included are population figures for the following attributes: Total Population, English, French, Non-Official, English and French, English and Non-Official Language, French and Non-Official Language, and English French and Non-Official Speaking. This data is also broken down by Age Group.

Introduction

This Canadian French 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 French-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 Canadian French 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.

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.

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.

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

Metadata

Rich metadata is available for each participant and conversation:

According to the Canadian government, approximately 2.54 million people residing in Montreal, in the province of Quebec, had French as their mother tongue in 2021. About 474,730 of them had English, the second official language, as their birth language. However, there were more people that year ( 522,255) whose mother tongue was an Indo-European language, such as German, Russian or Polish.

Introduction

This Canadian French 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 French -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 Canadian French 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.

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.

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.

These transcriptions streamline ASR and NLP development for French real estate voice applications.

Metadata

Detailed metadata accompanies each participant and conversation:

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:

Introduction

This Canadian French Call Center Speech Dataset for the Retail and E-commerce industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for French speakers. Featuring over 30 hours of real-world, unscripted audio, it provides authentic human-to-human customer service conversations vital for training robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, data scientists, and language model researchers to build high-accuracy, production-ready models across retail-focused use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Canadian French speakers. Captured in realistic scenarios, these conversations span diverse retail topics from product inquiries to order cancellations, providing a wide context range for model training and testing.

Topic Diversity

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

Such variety enhances your model’s ability to generalize across retail-specific voice interactions.

Transcription

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

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

Metadata

Rich metadata is available for each participant and conversation:

This granularity supports advanced analytics, dialect filtering, and fine-tuned model evaluation.

Usage and Applications

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

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

This Alberta Official Statistic compares the knowledge of languages among the Aboriginal Identity population in provinces and territories, based on self-assessment of the ability to converse in the language. Based on the 2011 National Household Survey (NHS), English is the most common language known by the Aboriginal Identity Population across Canada. In most provinces, nearly 100% of the Aboriginal Identity population can converse in English. The lowest proportion of English-speaking Aboriginal people is in Quebec, where the majority speak French. The highest proportion of Aboriginal people who speak Aboriginal languages was in Nunavut at 88.6%, followed by Quebec (32.4%) and the Northwest Territories (32.1%). In Alberta, more Aboriginal people are able to speak Aboriginal languages (15.1%) than are able to speak French or other (non-Aboriginal) languages. The proportion of Alberta Aboriginal people able to speak Aboriginal languages was sixth highest among provinces and territories.