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.

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.

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.

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.

This ZIP file contains an IVT file.

The Quebec general election was held on October 3, 2022 in Canada, to elect the 125 members of the 43rd legislature to the Quebec National Assembly. When asked a month before the election, 18 percent of residents of Montreal and Laval (a suburb of Montreal) considered language and Bill 96 to be the main issue in the campaign. The issue was most important to those whose mother tongue was English (32 percent).

Bill 96 is an act relating to the official language of Quebec, which came into effect in 2022, and aims to make French the only official and common language in Quebec.

This paper investigates the influence of the relative size of speech communities on language use in multilingual regions and cities. Due to peoples’ everyday mobility inside a city, it is still unclear whether the size of a population matters for language use on a sub-city scale. By testing the correlation between the size of a population and language use on various spatial scales, this study will contribute to a better understanding of the extent to which sociodemographic factors influence language use. The present study investigates two particular phenomena that are common to multilingual speakers, namely language mixing or Code-Switching and using multiple languages without mixing. Demographic information from a Canadian census will make predictions about the intensity of Code-Switching and language use by multilinguals in cities of Quebec and neighborhoods of Montreal. Geolocated tweets will be used to identify where these linguistic phenomena occur the most and the least. My results show that the intensity of Code-Switching and the use of English by bilinguals is influenced by the size of anglophone and francophone populations on various spatial scales such as the city level, land use level (city center vs. periphery of Montreal), and large urban zones on the sub-city level, namely the western and eastern urban zones of Montreal. However, the correlation between population figures and language use is difficult to measure and evaluate on a much smaller sub-urban scale such as the city block scale due to factors such as population figures missing from the census and people’s mobility. A qualitative evaluation of language use on a small spatial scale seems to suggest that other social influences such as the location context or topic of discussion are much more important predictors for language use than population figures. Methods will be suggested for testing this hypothesis in future research. I conclude that geographic space can provide us information about the relation between language use in multilingual cities and sociodemographic factors such as a speech community’s size and that social media is a valuable alternative data source for sociolinguistic research that offers new insights into the mechanisms of language use such as Code-Switching.

25% sample data.

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 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.

20% sample data.

CONTEXT AND OBJECTIVE: There is growing concern about understanding how sociodemographic variables may interfere with cognitive functioning, especially with regard to language. This study aimed to investigate the relationship between performance in the Brazilian version of the Montreal-Toulouse language assessment battery (MTL-BR) and education, age and frequency of reading and writing habits (FRWH).DESIGN AND SETTING: Cross-sectional study conducted in university and work environments in Rio Grande do Sul, Brazil.METHOD: The MTL-BR was administered to a group of 233 healthy adults, aged 19 to 75 years (mean = 45.04, standard deviation, SD = 15.47), with at least five years of formal education (mean = 11.47, SD = 4.77).RESULTS: A stepwise multiple linear regression model showed that, for most tasks, the number of years of education, age and FRWH were better predictors of performance when analyzed together rather than separately. In separate analysis, education was the best predictor of performance in language tasks, especially those involving reading and writing abilities.CONCLUSION: The results suggested that the number of years of education, age and FRWH seem to influence performance in the MTL-BR, especially education. These data are important for making diagnoses of greater precision among patients suffering from brain injuries, with the aim of avoiding false positives.

20% sample data.

Over the past fifty years, the proportion of Quebecers speaking both English and French has increased steadily, from 27.6 percent in 1971 to almost half the population (46.4 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 9.5 percent two decades later.

Guest lecture given at the University of Montreal (Université de Montréal, UdeM) 29 September, 2016. I render the notion and typological validity of the so called Second-Position Phenomena, i.e. principles of linearization sensitive to the distance from the clausal (or phrasal) left edge rather than to the type of the preceding syntactic category. In my talk, I discuss the interactions of clitic studies with word order typology and render the notions of clitic-external and clitic-internal ordering. Clitics clusterize in clausal-internal positions, not in clausal-edge positions. There exist at least 4 different types of word order systems with clustering clitics: W-systems, W+-systems, W*-systems and V-systems. Clitic-second languages (CL2 languages) and Verb-second languages (V2 languages) make up a class of 2P languages, cf. Roberts (2012) and Zimmerling (2015ab). Languages with endoclitics do not represent any shared syntactic system.

NoFA is a forced alignment model for Norwegian Bokmål, created by Nate Young (https://www.nateyoung.se/) for The Language Bank. This model is specifically made for the Montreal Forced Aligner (MFA), https://montreal-forced-aligner.readthedocs.io/. Forced alignment refers to algorithms that take an audio file with speech and an orthographic transcription of the speech as input and produce a phonetic transcript where each segment (each phone) is time-aligned with the audio file. NoFA is trained on The Language Bank's speech database NB Tale and the phonetic part of the RUNDKAST database developed at the Norwegian University of Science and Technology (NTNU). See the documentation file for further information about NoFA.

This repository provides word-level alignments for the Kathbath dataset [1], a multilingual speech corpus containing approximately 1500 hours of audio across 11 Indian languages.

The alignments were generated using the Montreal Forced Aligner (MFA) with pre-trained acoustic models specific to each language. To simplify reproducibility and save you the effort of running MFA yourself, we are releasing these alignments as part of our experimental setup.

If you find these alignments or any other aspect of our work useful, please consider citing the following paper:

• Anup Singh, Kris Demuynck, and Vipul Arora, "Language-Agnostic Speech Tokenizer for Spoken Term Detection with
  Efficient Retrieval", Interspeech 2025.

[1] IndicSUPERB: A Speech Processing Universal Performance Benchmark for Indian languages

Data Structure: