Optimizing Byte-Level Representation for Automatic Speech Recognition
Challenges in Multilingual ASR
End-to-end neural networks for automatic speech recognition (ASR) face challenges with support for multiple languages and large character sets like Chinese, Japanese, and Korean. This impacts compute resources and memory usage.
Previous Approaches
Previous attempts at addressing multilingual ASR challenges included byte-level representations and byte pair encoding (BPE) to mitigate longer sequences and decoding errors. However, these methods had limitations in ensuring accuracy.
State-of-the-Art Solution
Apple researchers have proposed a robust representation learning approach using a vector quantized auto-encoder, designed to optimize byte-level representation specifically for E2E ASR tasks. The method incorporates information from both text and audio, offering flexibility and effective error correction.
Proposed Method and Evaluation
The method formulates the representation problem as an optimization task with latent variables, using a vector quantized auto-encoder (VQ-AE) architecture. Evaluations on bilingual English and Mandarin dictation tasks showed consistent performance improvements over previous UTF-8 subword outputs.
Practical Applications and Value
This study presents a robust algorithm for optimizing byte-level representation in ASR, offering an alternative to UTF-8 representation. The proposed VQ-based approach showed a 5% relative reduction in Token Error Rate (TER) compared to UTF-8-based methods, highlighting its effectiveness and flexibility in multilingual ASR systems.
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