Speaker-Adaptation Speech Recognition Terminal Reducing Server Load

The patent extracts the speaker-adaptation functionality from the server side and relocates it to the terminal side. The terminal now performs statistical variable accumulation and conversion parameter generation locally, while the server only needs to provide basic speech recognition services. This extraction eliminates the need for server-side speaker ID management and pre-learning processes, thereby reducing data overload on the server while maintaining speaker-adaptation performance.

The terminal is empowered to perform speaker-adaptation independently through self-service mechanisms. It automatically accumulates statistical variables from recognized speech, generates conversion parameters, and applies them to improve recognition accuracy for the current speaker. This self-service capability eliminates the need for server-side pre-learning processes and speaker ID management, reducing both data volume and server resource consumption.

The system performs preliminary accumulation of statistical variables continuously in the background during normal speech recognition operations. Instead of requiring a separate pre-learning phase, the terminal gradually builds up speaker-specific statistical data from everyday speech interactions. This preliminary action occurs automatically without user intervention, maintaining high speaker-adaptation accuracy while eliminating operation complexity.

The terminal automatically performs speaker-adaptation without requiring user-initiated pre-learning processes. It self-manages the accumulation of statistical variables, generation of conversion parameters, and application of speaker-specific adaptations throughout normal operation. This self-service approach maintains high accuracy while completely eliminating the need for complex manual pre-learning operations.

The patent removes the speaker ID management function from the system architecture. Instead of implementing speaker identification and management mechanisms, the system directly performs speaker-adaptation using speech content analysis. The terminal extracts speaker-specific statistical variables from the speech itself without requiring separate speaker identification processes, thereby maintaining adaptation accuracy while reducing system complexity.

The system introduces statistical variables as an intermediary mechanism between speech input and speaker-adaptation output. Rather than directly implementing speaker ID management, the terminal uses accumulated statistical variables derived from speech content to enable speaker-specific adaptation. This intermediary approach maintains measurement precision for speaker characteristics while avoiding the complexity of direct speaker ID management systems.