Speaker-dependent-feature extraction, recognition and processing techniques
Speech Communication - Special issue on speaker characterization in speech terminology
Fundamentals of speech recognition
Fundamentals of speech recognition
Speech Communication - Special issue on speech under stress
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Emotions, speech and the ASR framework
Speech Communication - Special issue on speech and emotion
EURASIP Journal on Applied Signal Processing
EURASIP Journal on Audio, Speech, and Music Processing
Identifying speakers using their emotion cues
International Journal of Speech Technology
Audio based solutions for detecting intruders in wild areas
Signal Processing
Gender-dependent emotion recognition based on HMMs and SPHMMs
International Journal of Speech Technology
International Journal of Speech Technology
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In this paper, Suprasegmental Hidden Markov Models (SPHMMs) have been used to enhance the recognition performance of text-dependent speaker identification in the shouted environment. Our speech database consists of two databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. Our results show that SPHMMs significantly enhance speaker identification performance compared to Second-Order Circular Hidden Markov Models (CHMM2s) in the shouted environment. Using our collected database, speaker identification performance in this environment is 68% and 75% based on CHMM2s and SPHMMs, respectively. Using the SUSAS database, speaker identification performance in the same environment is 71% and 79% based on CHMM2s and SPHMMs, respectively.