Joint ASR-SD Neural Network for Speaker Diarization
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Solution Overview
Problem
Conventional systems that perform both speech recognition and speaker diarization on audio segments face challenges in accurately aligning outputs due to independent training of ASR and SD systems, leading to suboptimal results, especially in respecting word boundaries and effectively combining acoustic and linguistic cues.
Innovation Solution
A joint ASR-SD neural network that generates both transcription and speaker identification, trained to respect word boundaries and incorporate linguistic cues, producing high-quality speaker diarization outputs by conditioning on both acoustic and linguistic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate ASR and SD systems are used and trained independently, then each system can be optimized individually, but the alignment between ASR and SD outputs becomes difficult and suboptimal
Solution Approach 1:
The patent merges separate ASR and SD systems into a single joint model that processes acoustic data and generates both transcription and speaker diarization outputs simultaneously. This unified architecture eliminates the alignment problems between independent systems by ensuring both outputs are generated from the same processed representation and share common parameters through joint training.
2Device complexity
If SD systems operate independently on acoustic data only, then the system architecture remains simple, but word boundaries are not respected and alignment with ASR output deteriorates
Solution Approach 1:
The joint model combines ASR and SD processing in a unified architecture where the SD component operates on the same processed acoustic representation used by ASR. This ensures that speaker diarization outputs naturally respect word boundaries since both tasks share the same temporal and structural understanding of the speech signal.
3Measurement precision
If acoustic cues and linguistic cues are combined, then speaker diarization quality improves, but the system complexity increases
Solution Approach 1:
The joint model integrates acoustic cue processing and linguistic cue processing into a unified framework. The model takes acoustic input, processes it through shared layers, and branches into ASR and SD outputs. Linguistic cues from ASR predictions are fed back into the SD processing, allowing the system to leverage both acoustic and linguistic information without requiring separate complex pipelines.
4Ease of manufacture
If separate ASR and SD systems are used, then each system can be developed independently, but engineering overhead for post-processing alignment increases
Solution Approach 1:
By consolidating ASR and SD into a single joint model, the patent eliminates the need for post-processing alignment entirely. Both outputs are generated simultaneously from the same input processing pipeline, removing the time-consuming alignment step while maintaining the ability to independently train and optimize the joint system as a unified unit.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing audio data using neural networks.