Prosody Detection improves Pretrained Automatic Speech Recognition
Authors: David Sasu, Natalie Schluter


Reviewer paper summaries: This paper investigates the enhancement of ASR systems by integrating a prosody detection module. It introduces a joint ASR and prosody detection model, demonstrating significant improvements in prosody detection and ASR performance, particularly under limited resource conditions.
Summary of strengths: - The author introduces a multi-task model that combines pitch accent detection with the ASR task, improving the performance of pitch accent detection and ASR in limited resource settings.
Summary of weaknesses: - The paper does not provide enough motivation to introduce a prosody detection head to improve ASR.  The semi-supervised approach to prosody detection introduces noise, which may negatively impact the ASR performance on certain datasets. 

- wav2vec2 is not the SOTA model on the LibriSpeech dataset. More recent models like HuBERT, WavLM, data2vec, or data2vec 2.0 are better than wav2vec2.

- The study relies heavily on the BURNC dataset, which is relatively small and the improvement is inconsistant, potentially limiting the generalizability of the results.  I am quite skeptical of the conclusion that prosody cues enhance the performance of ASR.
Comments, suggestions and typos: - The paper does not adhere to the conventions of academic writing, with grammatical issues and undefined terminology.

- The abstract is lacking in clear research motivation, and the reference to a 2020 model as SOTA is outdated. The use of the term `prosody detection` to describe a basic binary classification method is too casual and imprecise.
Overall assessment scores: 2