Cross-Attention Speech-Language Models for Streaming Speech-to-Text
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Solution Overview
Problem
Existing speech-to-text processing systems face inefficiencies and inaccuracies due to the quadratic scaling of attention processing with the number of speech and text tokens, particularly in handling long speech utterances and integrating non-speech content, which is challenging for streaming applications.
Innovation Solution
A cross-attention speech-language (CASL) model that encodes speech inputs via audio embeddings and uses cross-attention processing with non-speech inputs, such as keywords or instructions, to improve accuracy and efficiency by reducing the number of processing operations, employing a combination of self-attention and residual connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional attention processing is used to integrate speech and text tokens, then accuracy in handling speech-to-text conversion is improved, but the number of processing operations increases quadratically with the number of tokens
Solution Approach 1:
The patent segments the attention processing into two distinct parts: self-attention for speech tokens only (N operations) and cross-attention for text tokens only (M operations). This segmentation eliminates the quadratic interaction term (N×M) that caused the computational bottleneck, while still maintaining the ability to integrate speech and text information through the cross-attention mechanism between the two separate attention streams.
Solution Approach 2:
The patent introduces a new dimensional structure by creating separate attention computation spaces for speech and text modalities. Instead of computing attention in a single joint space that requires O((N+M)²) operations, the model computes attention in two separate dimensions (O(N) for speech self-attention and O(M) for text cross-attention), then integrates the results through residual connections and layer normalization.
2Adaptability or versatility
If traditional attention processing is used to integrate speech and text tokens, then comprehensive integration of speech and non-speech content is achieved, but the computational complexity increases quadratically
Solution Approach 1:
The patent segments the attention computation into modality-specific components: self-attention operates exclusively on speech tokens to capture temporal dependencies within speech, while cross-attention operates exclusively on text tokens to integrate non-speech content. This segmentation reduces computational complexity from O((N+M)²) to O(N+M) while preserving the model's ability to integrate multiple content types through the cross-attention mechanism.
3Reliability
If traditional attention processing is used for long speech utterances, then complete processing of all speech tokens is achieved, but processing time increases significantly
Solution Approach 1:
The patent extracts the computationally intensive speech-text interaction operations from the traditional quadratic attention framework and isolates them into a separate cross-attention module that operates only on text tokens. This extraction allows the speech processing to be handled by efficient self-attention (O(N)) while text integration is handled by cross-attention (O(M)), significantly reducing processing time for long speech utterances while maintaining complete processing of all speech tokens through the self-attention mechanism.
Data Source
AI summary
Disclosed are apparatuses, systems, and techniques that implement training and deployment of cross-attention speech-language models for efficient processing of speech inputs. The techniques include processing, using a speech model, an audio input to generate audio embeddings and processing, using a text model, a text context associated with the audio input to generate output embeddings. The text model computes cross-attention states for the audio embeddings and text embeddings representative of the text context. The techniques further include providing, to a language model (LM), a prompt that includes output embeddings obtained based on the cross-attention states, and receiving, from the LM, a speech-to-text conversion of the audio input.