Cross-Attention Audio Stitching for Noisy Speech Response Accuracy
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Solution Overview
Problem
In noisy environments, automatic speech recognition (ASR) engines produce inaccurate transcriptions, leading to elongated and resource-intensive communications between users and interactive systems, as the errors propagate through generative models like large language models (LLMs), resulting in undesired responses.
Innovation Solution
Process both text embeddings derived from speech recognition and audio embeddings from captured audio data using a cross-attention mechanism within a transformer-based LLM to generate responses that accurately reflect user intent, even in noisy conditions, by interleaving audio and text embeddings through a cross-attention mechanism within the LLM decoder.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ASR engine processes voice input in noisy environment, then speech recognition is performed, but transcription accuracy deteriorates
Solution Approach 1:
The patent introduces audio embeddings as an intermediary representation that captures acoustic features directly from the audio signal, serving as a mediator between the noisy audio input and the text processing pipeline. This intermediary allows the system to access acoustic information without relying solely on potentially inaccurate ASR transcriptions, thereby resolving the contradiction between processing capability and accuracy.
Solution Approach 2:
The patent combines multiple types of embeddings (audio embeddings and text embeddings) into a composite representation that is processed by the LLM. This composite approach integrates the strengths of both acoustic feature extraction and text processing, enabling the system to maintain accuracy despite noisy input conditions by leveraging multiple data sources simultaneously.
2Productivity
If LLM processes noisy transcription, then response generation is performed, but response accuracy deteriorates
Solution Approach 1:
The patent uses audio embeddings as an intermediary that provides reliable acoustic information to the LLM, mediating between the potentially inaccurate text transcription and the response generation process. This intermediary ensures that the LLM receives complementary acoustic context that can correct or supplement the text-based understanding, thereby improving response accuracy without sacrificing generation productivity.
Solution Approach 2:
The patent changes the input parameters to the LLM by adding audio embedding vectors alongside text embeddings. This parameter change enriches the input representation with additional acoustic information, allowing the model to generate more accurate responses by considering both textual and acoustic dimensions of the input signal.
3Reliability
If cross-attention mechanism processes both audio and text embeddings, then response accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent merges audio embedding processing and text embedding processing into a unified LLM architecture that handles both modalities simultaneously through cross-attention mechanisms. This merging approach consolidates computational operations rather than processing audio and text separately, thereby improving response accuracy while controlling the increase in computational complexity through integrated processing.
Data Source
AI summary
Implementations relate to utilizing acoustic features of audio data that captures a user speech to help formulate a response that accurately respond to the user speech. In various implementations, text embedding(s) are generated based on processing a speech recognition of the user speech. The text embedding(s) can be processed using a multi-head attention of a transformer decoder, to generate intermediate attention features. In various implementations, the audio data of the user speech can be processed to generate audio embedding(s) that represent acoustic features of the audio data (e.g., whether the audio data, or a specific portion thereof, is noisy, etc.). The intermediate attention features and the audio embedding(s) can be provided to a cross-attention mechanism of the transformer decoder, to generate a model output from which the response to the user speech is derived.