High quality, real world speech datasets

English Speech Datasets

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Data Set Details

Hours available
50 hours

Age range
18 – 69

Download size
64GB

Number of speakers
64

Audio format
WAV

Accents
South African English

Dataset Demographics

Age range distribution

Recorders per age group
[18 – 29]: 20 Recorders
[30 – 40]: 29 Recorders
[50 – 69]: 9 Recorders

Gender Split

Recorders per gender
Male:  28 Recorders
Female:  28 Recorders

Hours Collected Across Domains

Runtime per domain
Retail: 12:27:41
Debt Collection: 12:35:53
Insurance: 12:21:50
Travel: 12:50:26
Total: 50:15:50
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[grdi_pie_chart show_card=”off” toolbar=”false” label_show=”false” chart_title=” ” title_alignment=”center” tooltip=”false” data_label=”true” datalabel_fontsize=”14px” datalabel_label_postfix=”%” legend_show=”false” element_count=”4″ category=”Retail, Debt Collection, Insurance, Travel” data_element_name=”25,25,25,25″ fill_color1=”#008ffb” fill_color2=”#01e396″ fill_color3=”#fe4560″ fill_color4=”#775dd0″ _builder_version=”4.19.0″ _module_preset=”default” _unique_id=”0ab2694a-9e63-4347-9282-947b5308c275″ custom_padding=”||0px||false|false” global_colors_info=”{}” _i=”1″ _address=”3.1.2.1″ /]

Additional Information

Education Level Distribution of English Call Recorders
Gender Split of English Call Recorders
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Gender Split of English Call Recorders Across Domains
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CONTACT SALES

Frequently Asked Questions about our

Speech Collection Services

Who uses your Machine Transcription Polishing service?

Our Machine Transcription Polishing service is ideal for large-volume orders on a B2B basis where automatic speech recognition needs proofreading under specific parameters in order for a client (usually a technology company) to test or improve their existing automated speech recognition systems. The service is not targeted at customers that want to improve auto-generated transcripts on a small scale. For this, we recommend using our Standard Transcription service in order to receive 99%+ accuracy guaranteed.

Who edits my machine-generated transcripts?

Once we understand the client’s exact needs and finalise terms of service, we source, assess and contract our MTP transcribers specifically for the client’s job. We ensure the team meets the agreed polishing requirements. Our reputation is based on a very selective process to contract a highly professional MTP transcription team. We match the client’s job requirements with MTP transcribers best suited to ensure client needs are met. For all work, we also provide a dedicated manager to oversee work on a daily basis.

How do you ensure accuracy?

Once the first transcribers are in place we start polishing the machine-generated transcripts. While doing so, we introduce a series of quality control steps in the workflow cycle to ensure all data processes for receiving, processing and returning client work are 100% in accordance with the agreed parameters. We also monitor all the processes to ensure strict adherence to prevailing and client-specific data protection requirements.

Do you sign Service Level Agreements?

For ongoing work, we prefer to work with an SLA. The SLA sets out a clear timetable that includes an initialisation period to set up the required team and logistics for client work. The SLA also covers terms and conditions related to the work and data privacy. If a client requires ongoing work, over an agreed period, Way With Words also usually provides a dedicated MTP team with management oversight, recruitment, selection, assessment, training processes and any other logistical assistance to aid the bespoke requirement.

Gender Split of English Call Recorders Across Domains

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[grdi_pie_chart show_card=”off” toolbar=”false” label_show=”false” chart_title=”Debt Collection” title_alignment=”center” data_label=”true” datalabel_fontsize=”14px” datalabel_label_postfix=”%” element_count=”2″ category=”Female,Male” data_element_name=”47,53″ fill_color1=”#17adb8″ fill_color2=”#8ebb14″ _builder_version=”4.19.0″ _module_preset=”default” _unique_id=”6fa43e79-782d-44ca-9599-61bc31b4cff7″ module_text_align=”left” text_orientation=”left” custom_margin=”||||false|false” global_colors_info=”{}” _i=”0″ _address=”7.1.1.0″ /]
[grdi_pie_chart show_card=”off” toolbar=”false” label_show=”false” chart_title=”Insurance” title_alignment=”center” data_label=”true” datalabel_fontsize=”14px” datalabel_label_postfix=”%” element_count=”2″ category=”Female,Male” data_element_name=”54,46″ fill_color1=”#17adb8″ fill_color2=”#8ebb14″ _builder_version=”4.19.0″ _module_preset=”default” _unique_id=”6fa43e79-782d-44ca-9599-61bc31b4cff7″ module_text_align=”left” text_orientation=”left” custom_margin=”||||false|false” global_colors_info=”{}” _i=”0″ _address=”7.1.2.0″ /]
[grdi_pie_chart show_card=”off” toolbar=”false” label_show=”false” chart_title=”Travel” title_alignment=”center” data_label=”true” datalabel_fontsize=”14px” datalabel_label_postfix=”%” element_count=”2″ category=”Female,Male” data_element_name=”49,51″ fill_color1=”#17adb8″ fill_color2=”#8ebb14″ _builder_version=”4.19.0″ _module_preset=”default” _unique_id=”6fa43e79-782d-44ca-9599-61bc31b4cff7″ module_text_align=”left” text_orientation=”left” custom_margin=”||||false|false” global_colors_info=”{}” _i=”0″ _address=”7.1.3.0″ /]
[grdi_column_chart show_card=”off” toolbar=”false” column_width=”55px” chart_title=”Education Level Distribution of English Call Recorders” title_alignment=”center” data_label=”true” datalabel_label_postfix=”%” category=”High School, Undergraduate, Certificate, Diploma, Graduate” data_element_name1=” ” data_element_value1=”27,24,1,5,43″ fill_color1=”rgba(55,168,255,0.85)” responsive_chart_direction=”1″ _builder_version=”4.19.0″ _module_preset=”default” _unique_id=”0620e447-2a16-45dd-a81c-39ef4ec40ef4″ background_color=”#FFFFFF” border_radii=”on|15px|15px|15px|15px” global_colors_info=”{}” _i=”0″ _address=”8.0.0.0″ /][grdi_column_chart show_card=”off” toolbar=”false” column_width=”55px” stacked=”true” grid_line=”false” chart_title=”Gender Split of English Call Recorders Across Domains” title_alignment=”center” data_label=”true” datalabel_label_postfix=”%” yaxis_label=”false” element_count=”2″ category=”Retail, Debt Collection, Insurance, Travel” data_element_name1=” Male” data_element_value1=”40,53,46,51″ fill_color1=”#01e396″ data_element_name2=”Female” data_element_value2=”60,47,54,49″ fill_color2=”#008ffb” responsive_chart_direction=”1″ _builder_version=”4.18.1″ _module_preset=”default” _unique_id=”0620e447-2a16-45dd-a81c-39ef4ec40ef4″ background_color=”#FFFFFF” border_radii=”on|15px|15px|15px|15px” global_colors_info=”{}” _i=”1″ _address=”8.0.0.1″ /][grdi_column_chart show_card=”off” toolbar=”false” column_width=”55px” grid_line=”false” chart_title=”Gender Split of English Call Recorders Across Domains” title_alignment=”center” data_label=”true” datalabel_label_postfix=”%” yaxis_label=”false” element_count=”2″ category=”Retail, Debt Collection, Insurance, Travel” data_element_name1=” Male” data_element_value1=”40,53,46,51″ fill_color1=”#01e396″ data_element_name2=”Female” data_element_value2=”60,47,54,49″ fill_color2=”#008ffb” responsive_chart_direction=”1″ _builder_version=”4.18.1″ _module_preset=”default” _unique_id=”0620e447-2a16-45dd-a81c-39ef4ec40ef4″ background_color=”#FFFFFF” border_radii=”on|15px|15px|15px|15px” global_colors_info=”{}” _i=”2″ _address=”8.0.0.2″ /]