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…

Space-Efficient Representation of Entity-centric Query Language Models

Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries. However, spoken entity recognition is a difficult problem, due to the large number of frequently-changing named entities. In addition, resources available for recognition are constrained when ASR is performed on-device. In this work, we investigate…

September 3, 2022

In "FAANG"

Integrating Categorical Features in End-To-End ASR

All-neural, end-to-end ASR systems gained rapid interest from the speech recognition community. Such systems convert speech input to text units using a single trainable neural network model. E2E models require large amounts of paired speech text data that is expensive to obtain. The amount of data available varies across different…

September 3, 2022

In "FAANG"

Revisiting ASR Error Correction with Specialized Models

Language models play a central role in automatic speech recognition (ASR), yet most methods rely on text-only models unaware of ASR error patterns. Recently, large language models (LLMs) have been applied to ASR correction, but introduce latency and hallucination concerns. We revisit ASR error correction with compact seq2seq models, trained…

July 7, 2026

In "FAANG"

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