Please list any teams members that already showed interest to work on this challenge. You can also keep this section empty.
Member 1 Esthi Yu
Member 2 Raphaella Baur
Member 3 Caleb Jung*
Team Open To Others?
Please specify if your team is open for other BaselHack participants to join. BaselHack recommends team sizes of about 5 persons. A team needs to have at least 2 members and at most 10 members.
We explore how we can automatically process natural language to analyse and create media content and how recent advances in deep learning can affect how we work, consume, and interact with natural language in the future.
[Yes]
Challenge Pitch
This challenge is to explain Lineare Algebra in Swiss German evaluate how Speech-to-Text performs in real-world scenarios. We are developing an Artificial Intelligence like Siri which offers a faster, easier way to get things done on Apple devices with OpenAPI. Millions of people now use Siri to send a message, play their favorite song or even take a selfie. We are using NLP and Backpropagation that demonstrates active listening, integrity and acute attention to detail.
Datasets
If applicable, please state what datasets are publicly avaialable or what private datasets you can bring to thehackathon.``
We have some datasets from different languages which are memes and Swiss German Languages. Generate a high-quality data set. There is a lack of speech datasets for Swiss-German texts. This is why the first part of this project will focus on generating a high-quality data set of 26 different Swiss German dialects and comments to detect stance and emotions. For this we will use an appropriate crowdsourcing setup informed by previous work on crowdsourcing similar tasks.
[ ] Build an AI that is fluent in Swiss German language with excellent comprehension, grammar and proofreading skills.
[ ] Excellent active listening skills with the ability to understand verbal nuances in the Swiss German dialect, including the variations across different provinces and territories.
[ ] Demonstrates attention to detail and critical thinking skills in evaluation of Siri’s language usage and dialect.
[ ] Ability to independently navigate systems and tools.
[ ] Demonstrates integrity and focuses on customer privacy
[ ] Content review or a similar environment, prior testing experience preferred
[ ] Demonstrates flexibility and adaptability to changing work-flows and responsibilities
[ ] Experience as an end user of Siri, similar Intelligent Personal Assistants, or other AI
[ ] Successfully meets or exceeds targets, working within tight deadlines
[ ] Ability to identify process solutions that increase efficiencies
[ ] Ability to maintain focus and aim for high results through large volumes of datasets.
P.S.
Please have a look at the following example or resources for further feedback.
Challenge Name
Swiss German Speech-to-Text NLP Bot
Team Members
Please list any teams members that already showed interest to work on this challenge. You can also keep this section empty.Team Open To Others?
Please specify if your team is open for other BaselHack participants to join. BaselHack recommends team sizes of about 5 persons. A team needs to have at least 2 members and at most 10 members.We explore how we can automatically process natural language to analyse and create media content and how recent advances in deep learning can affect how we work, consume, and interact with natural language in the future.
[Yes]
Challenge Pitch
This challenge is to explain Lineare Algebra in Swiss German evaluate how Speech-to-Text performs in real-world scenarios. We are developing an Artificial Intelligence like Siri which offers a faster, easier way to get things done on Apple devices with OpenAPI. Millions of people now use Siri to send a message, play their favorite song or even take a selfie. We are using NLP and Backpropagation that demonstrates active listening, integrity and acute attention to detail.
Datasets
If applicable, please state what datasets are publicly avaialable or what private datasets you can bring to thehackathon.``We have some datasets from different languages which are memes and Swiss German Languages. Generate a high-quality data set. There is a lack of speech datasets for Swiss-German texts. This is why the first part of this project will focus on generating a high-quality data set of 26 different Swiss German dialects and comments to detect stance and emotions. For this we will use an appropriate crowdsourcing setup informed by previous work on crowdsourcing similar tasks.
Datasets:
Datasets
Goals:
[ ] Build an AI that is fluent in Swiss German language with excellent comprehension, grammar and proofreading skills.
[ ] Excellent active listening skills with the ability to understand verbal nuances in the Swiss German dialect, including the variations across different provinces and territories.
[ ] Demonstrates attention to detail and critical thinking skills in evaluation of Siri’s language usage and dialect.
[ ] Ability to independently navigate systems and tools.
[ ] Demonstrates integrity and focuses on customer privacy
[ ] Content review or a similar environment, prior testing experience preferred
[ ] Demonstrates flexibility and adaptability to changing work-flows and responsibilities
[ ] Experience as an end user of Siri, similar Intelligent Personal Assistants, or other AI
[ ] Successfully meets or exceeds targets, working within tight deadlines
[ ] Ability to identify process solutions that increase efficiencies
[ ] Ability to maintain focus and aim for high results through large volumes of datasets.
P.S.
Please have a look at the following example or resources for further feedback.
SwissGerman_Dictionary