IoT Voice Uniqueness via Contextual Speaker Embedding Selection
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Solution Overview
Problem
Current virtual assistant devices generate robotic and emotionless voices, lacking emotional connection with users, as they do not provide unique voices for individual IoT devices, leading to a lack of user attachment and engagement.
Innovation Solution
A method that preprocesses user commands using natural language processing to classify speech and non-speech information, generates contextual parameters, and selects speaker embeddings from a database to assign distinct voices to IoT devices based on device-specific information and acoustic data, infusing human emotions into the voice responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a standard Text to Speech system is used for all devices, then the system is simple and easy to implement, but all devices produce identical robotic voices without emotional connection
Solution Approach 1:
The voice generation system is segmented into multiple independent components: a database storing diverse speaker embeddings (different voices, tones, emotions), a selection module that chooses appropriate embeddings based on device context, and a TTS processing module. This segmentation allows each component to be optimized independently while achieving overall voice uniqueness across devices.
Solution Approach 2:
Each IoT device is assigned locally optimized voice characteristics through device-specific parameters such as device type, location, usage context, and user preferences. The system selects speaker embeddings that match these local qualities, ensuring each device has a unique voice identity tailored to its specific context rather than using a uniform voice for all devices.
2Reliability
If device-specific voice assignments are implemented, then user emotional connection improves, but the system complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary action by pre-collecting and storing diverse speaker embeddings in a database during an offline preparation phase. These embeddings capture various voices, tones, and emotions. During runtime, the system only needs to select from these pre-prepared embeddings based on device context, significantly reducing online computational complexity while maintaining voice uniqueness and emotional connection.
Solution Approach 2:
The system uses speaker embeddings as simplified copies or representations of complex human voice characteristics. Instead of generating voices from scratch or using heavy neural networks for each device, the system stores and retrieves pre-extracted speaker embeddings that capture essential voice qualities, reducing processing complexity while preserving emotional and unique voice characteristics.
3Adaptability or versatility
If multiple speaker embeddings are stored and selected dynamically, then voice personalization improves, but data storage and selection processing increase
Solution Approach 1:
The system extracts only the essential voice characteristics needed for personalization by storing speaker embeddings that capture key acoustic features rather than storing complete audio recordings or complex voice models. This extraction approach maintains voice personalization capabilities while significantly reducing data storage requirements compared to storing full audio datasets.
Data Source
AI summary
A method in an interactive computing-system includes pre-processing an input natural-language (NL) from a user command based on natural language processing (NLP) for classifying speech information and non-speech information, obtaining an NLP result from the user command, fetching a device specific information from one or more IoT devices operating in an environment based on the NLP result, generating one or more contextual parameters based on the NLP result and the device specific information, selecting at least one speaker embedding stored in a database for the one or more IoT devices based on the one or more contextual parameters, and outputting the selected at least one speaker embedding for playback to the user.