English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In Speech Emotion Recognition (SER), textual data is often used alongsideaudio signals to address their inherent variability. However, the reliance onhuman annotated text in most research hinders the development of practical SERsystems. To overcome this challenge, we investigate how Automatic SpeechRecognition (ASR) performs on emotional speech by analyzing the ASR performanceon emotion corpora and examining the distribution of word errors and confidencescores in ASR transcripts to gain insight into how emotion affects ASR. Weutilize four ASR systems, namely Kaldi ASR, wav2vec2, Conformer, and Whisper,and three corpora: IEMOCAP, MOSI, and MELD to ensure generalizability.Additionally, we conduct text-based SER on ASR transcripts with increasing worderror rates to investigate how ASR affects SER. The objective of this study isto uncover the relationship and mutual impact of ASR and SER, in order tofacilitate ASR adaptation to emotional speech and the use of SER in real world.

ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition