Script structure
The script can also be customized to meet the needs of the project, so it is advisable to seek the help of speech therapists to design the flow of text. If the ML model has to be trained on well-structured data, it has to take into consideration the script and workflow.
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Scripted vs Unscripted
You can choose between using a scripted text or a natural or unscripted text to be read by the participants.
In a scripted text speech, the participants read what is displayed on the screen. This method is, mostly, used to record commands or instructions.
For example – ‘Turn off the music,’ ‘Press 1 to record.’
In the unscripted speech, the participants are given scenarios and asked to frame their sentences and speak as naturally as possible.
For example – ‘Can you please tell me where the next gas station is?’
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Utterance Collection / Wakeup Words
In case scripted text is used, you have to decide the number of scripts that will be used, and whether each participant will be reading a unique script or a group of scripts. Also, determine if the script contains a collection of wake words and commands.
For example –
Command 1:
“Alexa, what is the recipe for a chocolate cupcake?”
“Ok Google, what is the recipe for a chocolate cupcake?”
“Siri, what is the recipe for a chocolate cupcake?”
Command 2:
“Alexa, when is the flight to New York?”
“Google, when is the flight to New York?”
“Siri, when is the flight to New York?”
Audio requirements and formats
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Audio Quality
The quality of the recordings and the presence of background noise can impact the outcome of the project. But some speech data collections accept the presence of noise. However, it is advisable to have a better understanding of the requirements in terms of bit rate, signal-to-noise ratio, amplitude, and more.
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Format
The file format, data points, content structure, compression, and post-processing requirements also determine the quality of speech recordings.
The reason for the importance of file formats is that the model has to identify the file output and be trained to recognize that particular sound quality.
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Define Custom Audio Requirement
Custom audio requirements should be mentioned before the beginning of the collection process. Clients can choose customized audio files where specific files are clubbed together.
[Also Read: Enhance AI models with our quality Indian language audio datasets.]
Delivery and Processing Requirements
Once the speech data is gathered, the clients can choose to have it delivered according to their requirements.
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Transcription and Annotation requirement
Some clients require data transcription and labeling before they deliver. Additionally, they might also require specific forms of labeling and segmentation.
Sometimes it is better to seek speech-language pathologists and experts to help in transcribing speech in various languages to maintain the authenticity of the target language.
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File naming conventions
The data collection forms should specify any file naming convention to be followed. If the naming convention is complex or beyond the standard scope of the process, it could attract extra developmental costs.
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Delivery Guidelines
Security and delivery guidelines should be followed as specified in the project requirements. Moreover, if the data is to be delivered in small milestones or as a complete package at once should be specified. Clients also prefer timely progress monitoring updates so that they can keep track of the project status.
Leverage Advanced Data Augmentation Techniques
- Speech data augmentation can significantly expand the diversity and robustness of your dataset.
- Explore techniques like audio pitch shifting, time stretching, noise injection, and voice conversion to synthetically generate new, high-quality speech samples.
- Integrate these data augmentation methods into your speech data collection workflow to create a more comprehensive and representative dataset
Other Crucial Points to Note
The customizations will impact how,
- Data collection methods used
- The recruitment of participants
- The timeline for delivery
- The Tentative Cost of the project
Case Study: Multilingual Speech Data Collection
Shaip recently partnered with a leading conversational AI company to collect high-quality speech data in 12 languages for their virtual assistant platform. By leveraging our expertise in linguistic diversity and data collection best practices, we successfully delivered a comprehensive dataset that significantly improved the client’s speech recognition accuracy and user experience across multiple markets.
The Future of Speech Data Collection
As AI and ML technologies continue to advance, the demand for high-quality speech data will only continue to grow. Emerging trends, such as multilingual and multi-accent speech recognition, will require even more diverse and representative datasets. Additionally, the use of synthetic data and advanced data augmentation techniques will play an increasingly important role in expanding the size and variety of speech datasets.
At Shaip, we are committed to staying at the forefront of these trends and providing our clients with the highest quality speech data collection services to power their AI/ML innovations.
Conclusion
By following these 7 proven methods, you can design and execute a speech data collection project that sets your AI/ML applications up for success. Remember, the quality and diversity of your speech data are paramount, so be sure to invest the time and resources needed to create a dataset that truly meets your project’s requirements.
If you need further assistance in customizing and optimizing your speech data collection, the experts at Shaip are here to help. Contact us today to learn how our end-to-end data services can elevate your AI/ML capabilities.
[Also Read: Speech Recognition Training Data – Types, Data Collection, and Applications]