Compound Speech Models for Speaker Adaptation
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Solution Overview
Problem
Current speech recognition systems face challenges in accommodating multiple speakers with different characteristics, such as gender, age, and accent, as purely speaker-dependent models require extensive training and purely speaker-independent models are less accurate, leading to increased memory usage and complexity.
Innovation Solution
The development of compound models that incorporate both speaker-independent and speaker-dependent portions, allowing for efficient clustering and adaptation, reducing memory consumption and enabling robust speech processing across various speakers without the need for separate modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speaker-dependent models are used to improve speech recognition accuracy for specific speakers, then recognition accuracy is improved, but memory usage and model complexity increase significantly
Solution Approach 1:
The speech recognition system is segmented into two distinct components: speaker-independent models that handle general speech patterns and speaker-dependent models that handle individual speaker characteristics. This segmentation allows the system to load only necessary speaker-specific models into memory rather than storing complete speaker-dependent models for all possible speakers, thereby reducing memory usage while maintaining accuracy for recognized speakers.
Solution Approach 2:
The speaker-independent model serves as a universal base that can be applied to any speaker, handling general speech recognition tasks. This universal component reduces the need for separate complete models for each speaker, as the speaker-dependent adaptations can be applied on top of the shared speaker-independent framework, reducing overall memory requirements.
2Quantity of substance
If speaker-independent models are used to reduce memory usage and simplify the system, then memory consumption is reduced, but speech recognition accuracy deteriorates
Solution Approach 1:
The system dynamically adapts the speaker-independent model by applying speaker-dependent transformations when speaker characteristics are identified. This dynamic adaptation allows the system to maintain high accuracy for known speakers while keeping the base speaker-independent model lightweight for memory efficiency. The speaker-dependent parameters are applied on-demand rather than being permanently stored.
Solution Approach 2:
The system changes parameters of the speaker-independent model based on detected speaker characteristics. By modifying model parameters dynamically according to speaker identity rather than maintaining separate complete models, the system achieves speaker-specific accuracy while keeping memory usage low through parameter adaptation rather than structural duplication.
3Measurement precision
If separate speaker-dependent models are trained for each speaker to achieve high accuracy, then speech recognition accuracy is improved, but device complexity and training requirements increase
Solution Approach 1:
The model architecture is segmented into a shared speaker-independent component and speaker-dependent adaptation layers. This segmentation simplifies the overall architecture by avoiding the need for completely separate models for each speaker, reducing device complexity while maintaining the ability to achieve high accuracy through targeted speaker-specific adaptations.
Solution Approach 2:
The system merges the speaker-independent model with speaker-dependent adaptations into a unified framework. This combination reduces device complexity by consolidating multiple speaker-specific models into a single adaptable system, while still achieving high accuracy through the integrated speaker-dependent components that adjust the shared base model for individual speakers.
Data Source
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AI summary
An apparatus for providing compound models for speech recognition adaptation includes a processor. The processor may be configured to receive a speech signal corresponding to a particular speaker, select a cluster model including both a speaker independent portion and a speaker dependent portion based at least in part on a characteristic of speech of the particular speaker, and process the speech signal using the selected cluster model. A corresponding method and computer program product are also provided.