Conformer-S4 Speech Encoding for Long-Context Online Recognition
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges in real-time processing due to the limited context available for encoding speech, as they can only utilize past utterances (left context) and not future utterances, leading to inefficiencies in live speech transcription.
Innovation Solution
A structured state-space sequence model, referred to as S4, is initialized with a diagonal matrix of recurrent weights to enhance a conformer architecture, allowing it to process long left context and handle long-term dependencies, thereby improving the accuracy of live ASR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional transformer or conformer architectures are used for speech encoding, then the system structure is relatively simple, but the ability to process long left context and handle long-term dependencies is limited
Solution Approach 1:
The patent merges the S4 model with the conformer architecture to create an augmented encoder. The S4 model's state-space sequence processing capabilities are integrated with the conformer's existing structure, allowing the system to handle long left context while maintaining architectural coherence. This combination enables the encoder to process extended temporal sequences without completely redesigning the system.
Solution Approach 2:
The S4 model introduces a universal state-space processing mechanism that can handle various types of temporal dependencies within a single framework. The diagonal matrix of recurrent weights provides a flexible structure that can adapt to different context lengths and dependency patterns, making the encoder more versatile across different speech recognition scenarios.
2Measurement precision
If the encoder uses only past utterances (left context) for encoding, then real-time processing is enabled, but the encoding accuracy is reduced due to limited context
Solution Approach 1:
The S4 model with diagonal recurrent weights performs preliminary processing of long left context sequences, maintaining a compressed state representation that captures essential temporal dependencies. This preliminary action allows the encoder to efficiently utilize extended past context without requiring full processing of all historical utterances, thereby improving encoding accuracy while maintaining real-time performance.
3Adaptability or versatility
If the S4 model with diagonal matrix of recurrent weights is used, then long-term dependencies are handled effectively, but the model initialization and training complexity increases
Solution Approach 1:
The patent applies parameter changes by using a diagonal matrix for the recurrent weights in the S4 model. This parameterization simplifies the initialization process compared to full dense recurrent matrices, as diagonal matrices have fewer parameters and simpler spectral properties. The diagonal structure enables more stable training dynamics while still capturing long-term dependencies through the state-space formulation.
Data Source
AI summary
A method, device, and computer-readable storage medium for generating a text representation of a speech sample, including receiving an audio sample, encoding the audio sample based on left context of the audio sample with a structured state-space sequence model and a conformer, the structured state-space sequence model being initialized with a diagonal matrix of recurrent weights and trained with a set of training data, decoding the encoded audio sample, and generating a transcript of the audio sample based on the decoding.