Categories: FAANG

Optimizing Byte-level Representation for End-to-End ASR

In this paper, we propose an algorithm to optimize a byte-level representation for end-to-end (E2E) automatic speech recognition (ASR). Byte-level representation is often used by large scale multilingual ASR systems when the character set of the supported languages is large. The compactness and universality of byte-level representation allow the ASR models to use smaller output and therefore, provides more flexibility. UTF-8 is the most commonly used byte-level representation and has been successfully applied to ASR. However, it is not designed for ASR or any machine learning tasks. By using…
AI Generated Robotic Content

Recent Posts

Automated Feature Engineering in PyCaret

Automated feature engineering in

3 hours ago

Updating the Frontier Safety Framework

Our next iteration of the FSF sets out stronger security protocols on the path to…

3 hours ago

Adaptive Training Distributions with Scalable Online Bilevel Optimization

Large neural networks pretrained on web-scale corpora are central to modern machine learning. In this…

3 hours ago

Orchestrate seamless business systems integrations using Amazon Bedrock Agents

Generative AI has revolutionized technology through generating content and solving complex problems. To fully take…

3 hours ago

Helping our partners co-market faster with AI

At Google Cloud, we're deeply invested in making AI helpful to organizations everywhere — not…

3 hours ago

AMD’s Q4 revenue hits $7.66B, up 24% but stock falls

Advanced Micro Devices reported revenue of $7.658 billion for the fourth quarter, up 24% from…

4 hours ago