Danish Media Audio Dataset

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Project Overview:

Objective

The primary goal is to curate a comprehensive audio dataset that captures the diversity of the Danish media environment. This dataset aims to enable the development of AI models that can understand, analyze, and generate media content in Danish, enhancing accessibility and engagement for Danish-speaking audiences.

Scope

Covering a broad spectrum of media formats, the project targets a wide range of topics from current affairs and entertainment to sports and cultural discussions. The dataset is designed to reflect the richness and diversity of Danish media, ensuring a well-rounded representation of public discourse and communication styles.

Sources

Audio data is sourced from a variety of Danish media outlets, under strict agreements respecting copyright and intellectual property rights. This includes collaborations with leading news agencies, popular radio stations, and emerging podcast creators.

Data Collection Metrics

Total Audio Hours Collected: 2,000 hours
Number of Unique Audio Segments: 12,000
Source Diversity: 40% news, 30% entertainment, 20% sports, 10% culture
Languages: Primarily Danish, with segments in English and other Scandinavian languages

Annotation Process

Stages

Transcription: Converting audio segments to text, maintaining linguistic nuances.
Categorization: Classifying segments by genre, topic, and sentiment.
Timestamping: Marking specific time-coded data points for relevant audio cues and spoken words.

Annotation Metrics

Total Segments Annotated: 12,000
Total Annotations: 600,000
Average Annotations per Segment: 50

Quality Assurance

Stages

Transcription Verification: Ensuring that all transcribed audio maintains a 99% accuracy rate, reflecting linguistic nuances accurately.
Categorization Consistency Checks: Achieving a consistency level of 96% in classifying audio segments by genre, topic, and sentiment, ensuring reliable data categorization.
Timestamping Precision: Maintaining a precision rate of 98% in marking specific time-coded data points, which is crucial for the accurate positioning of audio cues and spoken words.

QA Metrics

Transcription Accuracy: 99%
Categorization Consistency: 96%
Timestamping Precision: 98%

Conclusion

The Danish Media Audio Dataset is an invaluable asset for advancing voice recognition technology in media-related applications. With its meticulously annotated and diverse collection of Danish audio recordings, it provides a rich resource for training sophisticated algorithms. This dataset not only aids in speech recognition and analysis but also supports the development of technology that enhances the accessibility and reach of Danish media content globally.