Probing Statistical Representations For End-To-End ASR

• URL: http://arxiv.org/abs/2211.01993v1
• Date: Thu, 3 Nov 2022 17:08:14 GMT
• Title: Probing Statistical Representations For End-To-End ASR
• Authors: Anna Ollerenshaw, Md Asif Jalal, Thomas Hain
• Abstract summary: This paper investigates cross-domain language model dependencies within transformer architectures using SVCCA. It was found that specific neural representations within the transformer layers exhibit correlated behaviour which impacts recognition performance.
• Score: 28.833851817220616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: End-to-End automatic speech recognition (ASR) models aim to learn a generalised speech representation to perform recognition. In this domain there is little research to analyse internal representation dependencies and their relationship to modelling approaches. This paper investigates cross-domain language model dependencies within transformer architectures using SVCCA and uses these insights to exploit modelling approaches. It was found that specific neural representations within the transformer layers exhibit correlated behaviour which impacts recognition performance. Altogether, this work provides analysis of the modelling approaches affecting contextual dependencies and ASR performance, and can be used to create or adapt better performing End-to-End ASR models and also for downstream tasks.

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