Analysis of human scream and its impact on text-independent speaker verification.

Scream is defined as sustained, high-energy vocalizations that lack phonological structure. Lack of phonological structure is how scream is identified from other forms of loud vocalization, such as "yell." This study investigates the acoustic aspects of screams and addresses those that are known to prevent standard speaker identification systems from recognizing the identity of screaming speakers. It is well established that speaker variability due to changes in vocal effort and Lombard effect contribute to degraded performance in automatic speech systems (i.e., speech recognition, speaker identification, diarization, etc.). However, previous research in the general area of speaker variability has concentrated on human speech production, whereas less is known about non-speech vocalizations. The UT-NonSpeech corpus is developed here to investigate speaker verification from scream samples. This study considers a detailed analysis in terms of fundamental frequency, spectral peak shift, frame energy distribution, and spectral tilt. It is shown that traditional speaker recognition based on the Gaussian mixture models-universal background model framework is unreliable when evaluated with screams.

[1]  Samantha J Barry,et al.  The automatic recognition and counting of cough , 2006, Cough.

[2]  John H. L. Hansen,et al.  Speaker Recognition by Machines and Humans: A tutorial review , 2015, IEEE Signal Processing Magazine.

[3]  John H. L. Hansen,et al.  Analysis and Compensation of Lombard Speech Across Noise Type and Levels With Application to In-Set/Out-of-Set Speaker Recognition , 2009, IEEE Transactions on Audio, Speech, and Language Processing.

[4]  John H. L. Hansen,et al.  Analysis and compensation of speech under stress and noise for environmental robustness in speech recognition , 1996, Speech Commun..

[5]  John H. L. Hansen,et al.  Speaker Identification Within Whispered Speech Audio Streams , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  R. H. Bernacki,et al.  Effects of noise on speech production: acoustic and perceptual analyses. , 1988, The Journal of the Acoustical Society of America.

[7]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[8]  Thierry Dutoit,et al.  Objective Study of Sensor Relevance for Automatic Cough Detection , 2013, IEEE Journal of Biomedical and Health Informatics.

[9]  John H. L. Hansen,et al.  A new perceptually motivated MVDR-based acoustic front-end (PMVDR) for robust automatic speech recognition , 2008, Speech Commun..

[10]  R. Lippmann,et al.  Multi‐style training for robust speech recognition under stress , 1986 .

[11]  John H. L. Hansen,et al.  Stress compensation and noise reduction algorithms for robust speech recognition , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[12]  A. Yarmey,et al.  Long-term auditory memory: speaker identification. , 1980, The Journal of applied psychology.

[13]  John H. L. Hansen,et al.  Duration mismatch compensation for i-vector based speaker recognition systems , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  J. Liénard,et al.  Effect of vocal effort on spectral properties of vowels. , 1999, The Journal of the Acoustical Society of America.

[15]  John H. L. Hansen,et al.  Analysis and compensation of stressed and noisy speech with application to robust automatic recognition , 1988 .

[16]  J. M. Pickett,et al.  Effects of Vocal Force on the Intelligibility of Speech Sounds , 1956 .

[17]  D. B. Paul.  Training of HMM recognizers by simulated annealing , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.