Frame-Level Vocal Effort Likelihood Space Modeling for Improved Whisper-Island Detection

In this study, a frame-based vocal effort likelihood space modeling framework for improved whisper-island detection within normally phonated audio streams is proposed. The proposed method is based on first training a traditional Gaussian mixture model for whisper and neutral speech, which is then employed to extract a newly proposed discriminative feature set entitled Vocal Effort Likelihood (VEL), for whisper-island detection. The VEL feature set is integrated within a BIC/T-BIC segmentation scheme for vocal effort change point(VECP) detection. With the dimension-reduced VEL 2-D feature set, the proposed framework has reduced computational costs versus prior method [1]. Experimental results using the UT-VocalEffort II corpus for whisper-island detection using the proposed framework are presented and compared with a previous algorithm introduced in [1]. The proposed algorithm is shown to improve performance in VECP detection with the lowest MultiError Score(MES) of 6.33. Furthermore, very accurate whisperisland detection was obtained using proposed algorithm, which is useful for sustained performance in speech systems (ASR, Speaker-ID, etc.)which might experience whisper speech. Finally, experimental performance achieves a 100% detection rate for the proposed algorithm, which represents the best whisperisland detection performance with lowest computational costs available in the literature to date.