Introduction

Welcome to the Australian English General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of English 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 Australian English communication.

Curated by FutureBeeAI, this 40 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 English speech models that understand and respond to authentic Australian accents and dialects.

Speech Data

The dataset comprises 40 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Australian English. 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 English speech and language AI applications:

Introduction

This Australian English 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 English speakers. Featuring over 40 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 40 hours of dual-channel call center recordings between native Australian English 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:

The National Spectral Database (NSD) houses data taken by Australian remote sensing scientists. The database includes spectra covering targets as diverse as mineralogy, soils, plants, water bodies and various land surfaces.
Currently the database holds spectral information from multiple locations across the country and as the collection grows in spatial / temporal coverage, the NSD will service continental scale validation requirements of the Earth observation community for satellite-based measurements of surface reflectance. The NSD is accessed with information provided at the NSD Geoscience Australia Content Management Interface (CMI) web page: https://cmi.ga.gov.au/data-products/dea/643/australian-national-spectral-database

Value: Curated spectral data provides a wealth of knowledge to remote sensing scientists. For other parties interested in calibration and validation (Cal/Val) of surface reflectance products, the Geoscience Australia (GA) Cal/Val dataset provides a useful resource of ground-truth data to compare to reflectance captured by Landsat 8 and Sentinel 2 satellites. The Aquatic Library is a robust collection of Australian datasets from 1994 to present time, primarily of end-member and substratum measurements. The University of Wollongong collection represents immense value in end-member studies, both terrestrial and aquatic.

Scope: The NSD covers Australian data including historical datasets as old as 1994. Physical study sites encompass locations around Australia, with spectra captured in every state.

Data types: - Spectral data: raw digital numbers (DN), radiance and reflectance. - From spectral bands VIS-NIR, SWIR1 & SWIR2: wavelengths 350nm - 2500nm collected with instruments in the field or lab setting.

Contact for further information: NSDB_manager@ga.gov.au

To view the entire collection click on the keyword "HVC 144490" in the below Keyword listing

Introduction

This Australian English Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 40 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for English -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 40 hours of dual-channel audio recordings between native Australian English speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

Growing Up in Australia: the Longitudinal Study of Australian Children (LSAC) is a major study following the development of 10,090 children and families from all parts of Australia. LSAC explores family and social issues while addressing a range of research questions about children’s development and wellbeing. The Wave 1 data collection was undertaken for AIFS by private social research companies Colmar Brunton Social Research and I-view/NCS Pearson. Data collection for Waves 2-6 was undertaken by the ABS. From 2004, participating families have been interviewed every two years, and between-wave mail-out questionnaires were sent to families in 2005 (Wave 1.5), 2007 (Wave 2.5) and 2009 (Wave 3.5). Additional between-wave questionnaires (Waves 4.5 and 5.5) were undertaken via online web forms from 2009 for the purposes of updating the contact details of study participants. The sampling unit of interest is the study child and there were two cohorts of children selected from children born within two 12-month periods: (1) B cohort ("Baby" cohort) - children born March 2003–February 2004, and (2) K cohort ("Kinder" cohort) - children born March 1999–February 2000. Please note that this release of LSAC is now superseded, and is available by request for approved training courses only. For the current release, please visit https://ada.edu.au/lsac_current

Time series seismograph data recorded from Australian National Seismograph Network (ANSN) observatories in Australia, islands in the Pacific, Southern and Indian Ocean's and the Australian Antarctic Territory.

Value: This data is used for earthquake monitoring, measurement, detection and location of earthquakes, which is valuable for emergency response, hazard modelling and mitigation. The dataset is also used to meet a subset of Australia's obligations to the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) to fulfil Australia's commitment to nuclear explosion monitoring.

Scope: Observatories in Australia, islands in the Pacific, Southern and Indian Ocean's and the Australian Antarctic Territory

Table 3. Big and giant species of the world (2.5–4 mm ‘large’ or> 4 ‘giant’): species list and maximum size. Afrobathynella Schminke, 1976; Allobathynella Morimoto & Miura, 1957; Arkaroolabathynella Abrams&King, 2013; Atopobathynella Schminke,1973; Billibathynella Cho,2005; Brevisomabathynella Cho, Park & Ranga Reddy, 2006; Chilibathynella Noodt, 1963; Kampucheabathynella Cho, Kry & Chhenh, 2015; Kimberleybathynella Cho, Park & Humphreys, 2005; Iberobathynella Schminke, 1973; Megabathynella Camacho & Abrams gen. nov.; Montanabathynella Camacho, Stanford & Newell, 2009; Lockyerenella Camacho & Little, 2017; Nipponbathynella Schminke, 1973; Notobathynella Schminke, 1973; Onychobathynella Camacho & Hancock, 2011; Paraeobathynella Camacho, 2005; Parabathynella Chappuis, 1926; Paraiberobathynella Camacho & Serban, 1998; Sinobathynella Camacho, Trontelj & Zagmajster, 2006. In bold the ‘giant’ species.

Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) is a major study following the development of approximately 10,000 young people and their families from all parts of Australia. It is conducted in partnership between the Department of Social Services, the Australian Institute of Family Studies and the Australian Bureau of Statistics with advice provided by a consortium of leading researchers. The study began in 2003 with a representative sample of children (who are now teens and young adults) from urban and rural areas of all states and territories in Australia. The study has a multi-disciplinary base, and examines a broad range of research questions about development and wellbeing over the life course in relation to topics such as parenting, family, peers, education, child care and health. It will continue to follow participants into adulthood. The study informs social policy and is used to identify opportunities for early intervention and prevention strategies. Participating families have been interviewed every two years from 2004, and between-wave mail-out questionnaires were sent to families in 2005 (Wave 1.5), 2007 (Wave 2.5) and 2009 (Wave 3.5). The B cohort (“Baby” cohort) of around 5,000 children was aged 0–1 years in 2003–04, and the K cohort (“Kinder” cohort) of around 5,000 children was aged 4–5 years in 2003–04. Study informants include the young person, their parents (both resident and non-resident), carers and teachers. Please note that this release of LSAC is now superseded, and is available by request for approved training courses only. For the current release, please visit https://ada.edu.au/lsac_current

Table 2. Taxa analysed, vouchers, and GenBank and ENA reference numbers.

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^A Sample was assembled from short-read archives, but could not be uploaded to GenBank because third-party assemblies require wet-lab experiments to meet requirements. Sequences are provided in the ribosomal alignment (see File S1.nex in the Supplementary material).

^B The only full-length ITS sequence obtained from Sanger sequencing.

^C The sample Evans 3270 was excluded from further analyses. Although the sample was resolved with E. pubescens (77% BS in RAxML tree, data not shown) as expected from its morphology, the sequence chromatograms were messy, resulting in ambiguous base calls, and the edited sequence still somewhat divergent from other E. pubescens. Because it was not possible to distinguish among hybridisation, paralogy or contamination as the cause of different base calls to other Erythrophleum haplotypes, the sequence was excluded from analyses. A second sample (Egan 2873) from the same locality (Cutta Cutta Caves, NT) produced a haplotype belonging to the E. pubescens clade, as expected from its morphological features, and it is likely that Evans 3270, likewise, represents E. pubescens; however a hybrid origin for this specimen with E. chlorostachys cannot be confidently excluded.

ABS Census data extract - G09 COUNTRY OF BIRTH OF PERSON BY AGE providing a breakdown of population at Suburb level and by:age groupscountry of birth of person(a)Australia(b)China (excludes SARs and Taiwan)(c)Hong Kong (SAR of China)(c)Born elsewhere(d)This data is based on place of usual residence.(a) This list consists of the most common 50 Country of Birth responses reported in the 2016 Census and 2011 Census.(b) Includes 'Australia', 'Australia (includes External Territories), nfd', 'Norfolk Island' and 'Australian External Territories, nec'.(c) Special Administrative Regions (SARs) comprise 'Hong Kong (SAR of China)' and 'Macau (SAR of China)'. (d) Includes countries not identified individually, 'Inadequately described', and 'At sea'. Excludes not stated.Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

The ecology and distribution of B. anthracis in Australia is not well understood, despite the continued occurrence of anthrax outbreaks in the eastern states of the country. Efforts to estimate the spatial extent of the risk of disease have been limited to a qualitative definition of an anthrax belt extending from southeast Queensland through the centre of New South Wales and into northern Victoria. This definition of the anthrax belt does not consider the role of environmental conditions in the distribution of B. anthracis. Here, we used the genetic algorithm for rule-set prediction model system (GARP), historical anthrax outbreaks and environmental data to model the ecological niche of B. anthracis and predict its potential geographic distribution in Australia. Our models reveal the niche of B. anthracis in Australia is characterized by a narrow range of ecological conditions concentrated in two disjunct corridors. The most dominant corridor, used to redefine a new anthrax belt, parallels the Eastern Highlands and runs from north Victoria to central east Queensland through the centre of New South Wales. This study has redefined the anthrax belt in eastern Australia and provides insights about the ecological factors that limit the distribution of B. anthracis at the continental scale for Australia. The geographic distributions identified can help inform anthrax surveillance strategies by public and veterinary health agencies.

Cytochrome b data file for all Galaxiella individuals sequencedCytochrome b data for all individuals included in our study in nexus format. Sequence names match the species initials and the field codes shown in locality code column in our locality data table. Samples lacking a field code used the first four letters of the waterbody name, while samples ending in gb were from GenBank.galaxiella.allspp.cytb.all.ind.nexS7 data file for GalaxiellaS7 data for all individuals included in our study in nexus format. Data were aligned with MAFFT and phased in DnaSP (each allele is indicated by .a or .b at the end of the OTU code). Sequence names match the species initials and the field codes shown in locality code column in our locality data table. Samples lacking a field code used the first four letters of the waterbody name, while samples ending in gb were from GenBank.galaxiella.s7.allspp.matft.fftnsi.byphase.nexArlequin analysis file (part 1 of 2) for Galaxiella pusilla Cytb sequencesThe inp...

Australia Iron Ore: Class A and B: Closing Stock data was reported at 51.850 Tonne bn in 2021. This records an increase from the previous number of 51.450 Tonne bn for 2020. Australia Iron Ore: Class A and B: Closing Stock data is updated yearly, averaging 17.950 Tonne bn from Dec 1989 (Median) to 2021, with 33 observations. The data reached an all-time high of 53.250 Tonne bn in 2014 and a record low of 12.700 Tonne bn in 2002. Australia Iron Ore: Class A and B: Closing Stock data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.ESG: Environmental: Mineral and Energy Resources: by Commodity: OECD Member: Annual. Class A refers to commercially recoverable resources; Class B refers to potentially commercially recoverable resources; Class C refers to non-commercial and other known deposits

Manufacturers in the Motor Vehicle Body and Trailer Manufacturing industry have faced mixed conditions over the past decade. After Australian car manufacturing collapsed in 2017, industry manufacturers were forced to significantly shift their operations and strategies. Motor vehicle body manufacturers have declined as a source of overall revenue, while RV manufacturing has filled the gaps. The pandemic added further volatility, constraining manufacturing output and generating sharp fluctuations in demand. However, Australia's international border closures during the pandemic were a boon to RV manufacturers, as interest in domestic holidays soared. Overall, revenue is expected to have climbed at an annualised 1.7% over the five years through 2024-25 to $6.7 billion. This includes an anticipated plummet of 6.2% in 2024-25 as cost-of-living pressures resulting from elevated interest rates and high inflation weigh on downstream demand. In 2022, the ACCC took regulatory action against Australian caravan manufacturers following an extensive review of consumer complaints. The review raised issues around the quality of information given to customers when they purchase a defective product. Some customers reported struggling to get properly reimbursed after finding faults with the caravan they purchased. Despite this high-profile action, locally manufactured caravans are more popular than ever. Consumer interest in domestic holidays surged during the pandemic, and domestically manufactured caravans are still overwhelmingly more popular than imported substitutes. Although demand has softened after a spike in 2022-23, industrywide profitability has risen amid falling input costs. Revenue is projected to hike over the coming years, but demand conditions will be challenged by rising outbound travel by Australians and robust import competition. These trends are set to make it more difficult for smaller manufacturers to survive, resulting in an uptick in industry exits. Overall, revenue is forecast to rise at an annualised 1.1% over the five years through 2029-30 to $7.0 billion.

Demographics for HIV-1 B subtype, non-B subtypes and sequences in a network including age, gender, AMEN centre and sequencing era.

Australia Crude Oil: Class A and B: Closing Stock data was reported at 0.743 Bar bn in 2023. This records a decrease from the previous number of 0.773 Bar bn for 2022. Australia Crude Oil: Class A and B: Closing Stock data is updated yearly, averaging 1.111 Bar bn from Dec 1988 (Median) to 2023, with 36 observations. The data reached an all-time high of 1.784 Bar bn in 1995 and a record low of 0.743 Bar bn in 2023. Australia Crude Oil: Class A and B: Closing Stock data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.ESG: Environmental: Mineral and Energy Resources: by Commodity: OECD Member: Annual. Class A refers to commercially recoverable resources; Class B refers to potentially commercially recoverable resources; Class C refers to non-commercial and other known deposits

Australia Copper: Class A and B: Closing Stock data was reported at 98.500 Tonne mn in 2021. This records an increase from the previous number of 96.250 Tonne mn for 2020. Australia Copper: Class A and B: Closing Stock data is updated yearly, averaging 41.850 Tonne mn from Dec 1989 (Median) to 2021, with 33 observations. The data reached an all-time high of 98.500 Tonne mn in 2021 and a record low of 6.700 Tonne mn in 1990. Australia Copper: Class A and B: Closing Stock data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Australia – Table AU.OECD.ESG: Environmental: Mineral and Energy Resources: by Commodity: OECD Member: Annual. Class A refers to commercially recoverable resources; Class B refers to potentially commercially recoverable resources; Class C refers to non-commercial and other known deposits

This collection includes Global Navigation Satellite System (GNSS) observations from short-term occupations at multiple locations across Australia and its external territories, including the Australian Antarctic Territory.

Value: The datasets within this collection are available to support a myriad of scientific applications, including research into the crustal deformation of the Australian continent.

Scope: Data from selected areas of interest across Australia and its external territories, including the Australian Antarctic Territory. Over time there has been a focus on areas with increased risk of seismic activity or areas with observed natural or anthropogenic deformation.

Access: The datasets within this collection are currently stored offline, to access please send a request to gnss@ga.gov.au

Coding framework: Drivers of lung cancer screening participation in Australia using the COM-B (capability, opportunity, motivation-behaviour) model.