Transformer ASR Sliding-Window Context Adaptation
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Solution Overview
Problem
End-to-end automatic speech recognition (ASR) systems face challenges in recognizing speakers with different characteristics and topics not present in the training data, as they rely on summarizing contextual information into fixed-dimensional vectors, which loses sequential patterns and hinders effective adaptation.
Innovation Solution
A context-expanded Transformer-based system that processes multiple utterances simultaneously, using a self-attention mechanism to adapt hidden vectors without summarizing information, improving recognition accuracy for long audio recordings like lectures and conversations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If contextual information is summarized into a single fixed-dimensional vector, then the system complexity is reduced, but information loss occurs and sequential patterns are not preserved
Solution Approach 1:
The patent segments the contextual information processing by maintaining separate hidden vector representations for each utterance in the sequence, rather than compressing all context into a single vector. Each utterance's hidden vectors are independently computed and then attended to, preserving detailed information while managing complexity through modular processing.
Solution Approach 2:
The patent introduces a new dimension by using sequence-of-vectors representation instead of a single fixed-dimensional vector. The contextual information is expanded from one-dimensional compression to multi-dimensional sequence structure, allowing the model to access both summarized and detailed information through the attention mechanism.
2Productivity
If a fixed-dimensional vector is used to represent previous utterances, then processing efficiency is improved, but speaker and topic adaptation capability deteriorates
Solution Approach 1:
The patent introduces an attention mechanism as an intermediary between the sequence of hidden vectors and the current utterance processing. This attention mechanism selectively weights and combines information from previous utterances, enabling efficient processing while maintaining adaptability to speaker and topic characteristics through dynamic information selection.
Solution Approach 2:
The patent makes the contextual representation dynamic by using variable-length sequences of hidden vectors that adapt to the number of previous utterances available. The attention weights are dynamically computed based on the relevance of each previous utterance, allowing the system to adapt efficiently to different speakers and topics without fixed-dimensional constraints.
3Measurement precision
If multiple utterances are processed simultaneously with self-attention, then recognition accuracy for long audio recordings is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into distinct encoder and decoder stages, with self-attention applied separately at each stage. The encoder processes input utterances to generate hidden vectors, and the decoder uses attention to these vectors for recognition. This segmentation reduces overall computational complexity compared to applying self-attention throughout the entire processing pipeline.
Solution Approach 2:
The patent applies self-attention selectively at specific locations in the processing architecture (encoder and decoder stages) rather than uniformly throughout. The attention mechanism is applied locally where it provides maximum benefit for capturing sequential patterns, while other parts of the system use more computationally efficient operations.
Data Source
AI summary
This invention relates generally to speech processing and more particularly to end-to-end automatic speech recognition (ASR) that utilizes long contextual information. Some embodiments of the invention provide a system and a method for end-to-end ASR suitable for recognizing long audio recordings such as lecture and conversational speeches. This disclosure includes a Transformer-based ASR system that utilizes contextual information, wherein the Transformer accepts multiple utterances at the same time and predicts transcript for the last utterance. This is repeated in a sliding-window fashion with one-utterance shifts to recognize the entire recording. In addition, some embodiments of the present invention may use acoustic and/or text features obtained from only the previous utterances spoken by the same speaker as the last utterance when the long audio recording includes multiple speakers.