On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition

Typically, three common approaches are used to address the issue of noisy This paper proposes an efficient attempt to noisy speech emotion speech emotion recognition (NSER): the signal level, the feature recognition (NSER). Conventional NSER approaches level, and the model level, as outlined by Tiwari et al have proven effective in mitigating the impact of artificial [2]. For instance, Pandharipande et al. [3] used a voice activity noise sources, such as white Gaussian noise, but are limited detector to reduce noise at the signal level. Lachiri et to non-stationary noises in real-world environments due to al. [4] introduced a novel approach involving MFCC-shifteddelta-cepstral their complexity and uncertainty. To overcome this limitation, coefficients at the feature level. Tiwari et al. [2] we introduce a new method for NSER by adopting the devised a generative noise model at the model level. The previously automatic speech recognition (ASR) model as a noise-robust mentioned studies have proven effective in mitigating feature extractor to eliminate non-vocal information in noisy the impact of common noise sources like white Gaussian speech. We first obtain intermediate layer information from noise on speech-related tasks. However, in real-world settings, the ASR model as a feature representation for emotional a distinct category of noise sounds, such as high-heeled speech and then apply this representation for the downstream shoes and door knocking, presents a substantial challenge.