Speaker Embedding Biasing for Atypical Speech Recognition
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Solution Overview
Problem
Existing deep learning-based speech conversion models struggle with overfitting to training data, leading to poor performance on speakers with atypical speech patterns, and require extensive training data and specialized computing resources for personalization.
Innovation Solution
A speech conversion system that trains a speaker embedding network and speech conversion model concurrently, enabling implicit clustering of speaker embeddings and sharing training data across speakers with similar acoustic-phonetic properties, allowing for scalable personalization without the need for separate models per speaker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based speech conversion models are trained on extensive training data to improve accuracy, then speech recognition accuracy improves, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent segments the speech conversion task into two distinct components: a speaker-agnostic acoustic model that handles general speech recognition, and a speaker embedding module that captures speaker-specific characteristics. This segmentation allows each component to be optimized independently, reducing overall model complexity while maintaining accuracy.
Solution Approach 2:
The patent creates a universal speaker embedding space that can be applied across multiple speakers and domains. The speaker embedding module serves multiple functions: it captures speaker identity, adapts to different speaking styles, and works across various domains without requiring separate models, thereby reducing complexity while improving generalization.
2Measurement precision
If separate models are trained for each speaker to achieve personalized speech recognition, then accuracy for atypical speech patterns improves, but computational burden and training resources increase
Solution Approach 1:
The patent merges multiple speaker-specific models into a single unified model by introducing a speaker embedding module that dynamically adapts the shared acoustic model to different speakers. Instead of maintaining separate models for each speaker, the system combines them into one model that can handle multiple speakers through embedding-based adaptation, significantly reducing computational burden.
Solution Approach 2:
The patent changes the parameter representation from speaker-specific model weights to speaker embeddings that are inserted into the model. This parameter transformation allows the system to adapt to different speakers by changing embedding vectors rather than loading different model parameters, reducing computational resources while maintaining personalization accuracy.
3Adaptability or versatility
If training data is increased to cover diverse speakers and domains, then generalization to unseen data improves, but data collection and processing requirements increase
Solution Approach 1:
The patent introduces speaker embeddings as an intermediary representation that bridges the gap between limited training data and diverse test scenarios. The embeddings capture speaker-specific characteristics in a compressed form, allowing the model to generalize to unseen speakers and domains without requiring extensive training data for each specific case.
Solution Approach 2:
The patent adds a new dimension to the model by incorporating speaker embeddings as an additional input modality. This dimensional addition allows the model to leverage speaker-specific information without increasing the volume of training data, enabling better generalization through a different representation space.
4Measurement precision
If model parameters are optimized for canonical speech patterns, then performance on typical speakers improves, but performance on atypical speech patterns deteriorates
Solution Approach 1:
The patent makes the model dynamic by introducing speaker embeddings that can be adjusted based on the specific speaker being processed. Instead of a static model optimized for canonical speech, the system dynamically adapts to different speakers including atypical ones, maintaining performance across diverse speech patterns through embedding-based personalization.
Data Source
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AI summary
A method (400) includes receiving a reference audio signal corresponding to reference speech spoken by a target speaker with atypical speech, and generating, by a speaker embedding network (250) configured to receive the reference audio signal as input, a speaker embedding (350) for the target speaker. The speaker embedding conveys speaker characteristics of the target speaker. The method also includes receiving a speech conversion request that includes input audio data (102) corresponding to an utterance spoken by the target speaker associated with the atypical speech. The method also includes biasing, using the speaker embedding generated for the target speaker by the speaker embedding network, a speech conversion model (210) to convert the input audio data corresponding to the utterance spoken by the target speaker associated with atypical speech into an output canonical representation of the utterance spoken by the target speaker.