Heterogeneous Audio Processing Architecture for Speaker Identification
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Solution Overview
Problem
Existing speaker recognition technologies are not optimally suited for energy-efficient operation in various environments and use scenarios, particularly in devices with limited power sources like mobile phones, and struggle with unobtrusive training and efficient identification in real-time conditions.
Innovation Solution
A heterogeneous architecture with a first processing unit handling frequently performed, low-power audio processing tasks and a second unit handling less frequent, more power-intensive tasks, along with unobtrusive training methods using audio segments from everyday conversations and channel compensation techniques, enables efficient speaker identification and model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speaker recognition technology processes audio signals in real-time with comprehensive analysis, then identification accuracy is improved, but power consumption increases
Solution Approach 1:
The audio processing pipeline is segmented into multiple stages: audio strength testing, speech detection, frame quality assessment, and speaker model matching. Each stage filters and processes only necessary portions of the audio signal, avoiding comprehensive analysis at every step. This segmentation allows the system to maintain high identification accuracy while reducing overall power consumption by eliminating redundant processing.
Solution Approach 2:
The system performs partial processing by applying admission tests and quality thresholds that filter out low-quality speech frames before they undergo full speaker model matching. By performing only the necessary portion of processing on each audio frame (skipping full analysis for frames that fail quality checks), the system maintains accuracy for valid speech while reducing power consumption from unnecessary processing of invalid or low-quality frames.
2Reliability
If the system continuously monitors and processes all audio signals for speaker identification, then identification reliability is improved, but device battery life deteriorates
Solution Approach 1:
The system implements periodic processing by continuously monitoring audio strength and speech presence at regular intervals, then performing full speaker model matching only when speech is detected and quality thresholds are met. This periodic approach ensures reliable speaker identification when needed while allowing the system to enter lower-power states during intervals without speech, thereby extending battery life.
Solution Approach 2:
The system performs preliminary audio strength testing and speech detection before committing to full speaker model matching. These preliminary actions filter out non-speech or low-quality audio segments, ensuring that computationally intensive processing is only applied to promising candidates. This maintains identification reliability by pre-screening inputs while conserving battery power by avoiding unnecessary full processing of unsuitable audio segments.
3Measurement precision
If the system applies rigorous quality tests and admission criteria to speech frames, then speaker model accuracy is improved, but processing time increases
Solution Approach 1:
The quality assessment process is segmented into multiple independent tests: audio strength testing, speech detection, frame quality assessment, and admission testing. Each test operates on different aspects of the audio signal and can be performed in parallel or selectively. This segmentation allows the system to apply rigorous quality criteria for accurate speaker modeling while optimizing processing time by skipping tests for frames that fail early criteria or processing tests concurrently where possible.
Data Source
AI summary
Functionality is described herein for recognizing speakers in an energy-efficient manner. The functionality employs a heterogeneous architecture that comprises at least a first processing unit and a second processing unit. The first processing unit handles a first set of audio processing tasks (associated with the detection of speech) while the second processing unit handles a second set of audio processing tasks (associated with the identification of speakers), where the first set of tasks consumes less power than the second set of tasks. The functionality also provides unobtrusive techniques for collecting audio segments for training purposes. The functionality also encompasses new applications which may be invoked in response to the recognition of speakers.