Masked Audio Embeddings for Overlapping Speech Recognition
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately recognizing and transcribing overlapping speech from multiple speakers, particularly in real-time applications where latency and accuracy are critical.
Innovation Solution
The method involves training a speech recognition model using a loss function that generates masked audio embeddings for each speaker. By applying masking losses based on known start and end times of overlapping regions, the model enforces that each embedding corresponds only to the audio spoken by its respective speaker.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech recognition systems process overlapping speech from multiple speakers, then the system can handle real-time communication scenarios, but the recognition accuracy deteriorates due to speaker interference and misalignment
Solution Approach 1:
The patent segments the audio signal by creating separate masked audio embeddings for each speaker. The masking mechanism divides the mixed audio signal into speaker-specific components by applying masking losses that enforce each embedding corresponds only to its respective speaker's audio, thereby separating overlapping speech segments for accurate recognition
Solution Approach 2:
The patent introduces masked audio embeddings as intermediary representations between the raw audio signal and the final speech recognition output. These masked embeddings act as mediators that isolate speaker-specific information from the mixed audio signal, enabling accurate transcription of overlapping speech by preventing cross-speaker interference
2Productivity
If the speech recognition model processes overlapping speech regions, then complete transcription coverage is achieved, but computational complexity increases due to multiple speaker embeddings and masking operations
Solution Approach 1:
The patent creates a universal masked audio embedding generation mechanism that handles both single-speaker and multi-speaker scenarios through the same architecture. The masking loss function and embedding generation process work universally across different speech conditions, providing comprehensive transcription coverage without requiring separate processing paths for different speaker configurations
Data Source
AI summary
A method for training a speech recognition model with a loss function includes receiving an audio signal including a first segment corresponding to audio spoken by a first speaker, a second segment corresponding to audio spoken by a second speaker, and an overlapping region where the first segment overlaps the second segment. The overlapping region includes a known start time and a known end time. The method also includes generating a respective masked audio embedding for each of the first and second speakers. The method also includes applying a masking loss after the known end time to the respective masked audio embedding for the first speaker when the first speaker was speaking prior to the known start time, or applying the masking loss prior to the known start time when the first speaker was speaking after the known end time.