Alexa Wake Words in US English

Project Overview:

Objective

As a leading data collection and annotation company, we successfully built a comprehensive dataset of audio clips featuring the “Alexa” wake word in US English. This dataset is instrumental in advancing wake word detection systems and voice assistant technologies.

Scope

Our project involved gathering a wide range of audio recordings in different acoustic environments and accents. We meticulously annotated these recordings for the “Alexa” wake word, demonstrating our expertise in handling complex data annotation tasks.

Sources

Voice Assistant Users: We collaborated with Alexa users, who generously contributed audio clips of them uttering “Alexa” under various scenarios.
Voice Actors: To ensure diversity, we engaged professional voice actors to create synthetic wake-word recordings.
Public Domain Recordings: Our team also sourced publicly available audio that contained the “Alexa” wake word.

Data Collection Metrics

Total Audio Clips Collected and Annotated: 50,000
User Contributions: 30,000 clips
Voice Actor Recordings: 10,000 clips
Public Domain Extracts: 10,000 clips

Annotation Process

Stages

Wake Word Annotation: Each audio clip was precisely annotated to identify the “Alexa” wake word.
Speaker Demographics: We compiled metadata on speaker demographics, including accent, age, and gender.
Recording Conditions: Detailed documentation of recording conditions was maintained, such as background noise levels and acoustic environments.

Annotation Metrics

Audio Clips with Wake Word Annotations: 50,000
Speaker Demographics: 50,000
Recording Condition Metadata: 50,000

Quality Assurance

We adhered to strict quality assurance and privacy protocols. Annotation accuracy was verified through a rigorous multi-step process involving both automated tools and human reviewers. Additionally, we ensured that all user-contributed audio clips were used with explicit consent and anonymized to protect personally identifiable information. Our processes comply with the latest privacy regulations.

QA Metrics:

Annotation Validation Cases: 5,000 (10% of the total dataset)
Privacy Audits: Conducted on 30,000 user-contributed clips

Conclusion

Through this project, we have significantly contributed to the enhancement of wake word detection and voice assistant technologies. Our diverse recordings, detailed annotations, and commitment to privacy compliance underscore our capability as a premier data collection and annotation service provider. This case study exemplifies our expertise in delivering high-quality datasets for machine learning model training in various domains including audio, text, image, and video data.