Weitere Artikel dieser Ausgabe durch Wischen aufrufen
Speaker verification performance in neutral talking environment is usually high, while it is sharply decreased in emotional talking environments. This performance degradation in emotional environments is due to the problem of mismatch between training in neutral environment while testing in emotional environments. In this work, a three-stage speaker verification architecture has been proposed to enhance speaker verification performance in emotional environments. This architecture is comprised of three cascaded stages: gender identification stage followed by an emotion identification stage followed by a speaker verification stage. The proposed framework has been evaluated on two distinct and independent emotional speech datasets: in-house dataset and “Emotional Prosody Speech and Transcripts” dataset. Our results show that speaker verification based on both gender information and emotion information is superior to each of speaker verification based on gender information only, emotion information only, and neither gender information nor emotion information. The attained average speaker verification performance based on the proposed framework is very alike to that attained in subjective assessment by human listeners.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Chen, L., Lee, K. A., Chng, E.-S., Ma, B., Li, H., & Dai, L. R., (2016). Content-aware local variability vector for speaker verification with short utterance. In The 41st IEEE international conference on acoustics, speech and signal processing, Shanghai, China, March 2016 (pp. 5485–5489).
Emotional Prosody Speech and Transcripts dataset. (2016). Retrieved November 15, 2016, from http://www.ldc.upenn.edu/Catalog/CatalogEntry. jsp?catalogId = LDC2002S28.
Hansen, J. H. L., & Hasan, T., (2015). Speaker recognition by machines and humans: a tutorial review. IEEE Signal Processing Magazine, 32(6), 74–99. CrossRef
Harb, H., & Chen, L. (2003). Gender identification using a general audio classifier. In International Conference on Multimedia and Expo 2003 (ICME’03), July 2003, (pp. 733–736).
Lee, C. M., & Narayanan, S. S. (2005). Towards detecting emotions in spoken dialogs. IEEE Transactions on Speech and Audio Processing, 13(2), 293–303. CrossRef
Mary, L., & Yegnanarayana, B. (2008). Extraction and representation of prosodic features for language and speaker recognition. Speech Communication, 50(10), 782–796. CrossRef
Nwe, T. L., Foo, S. W., & De Silva, L. C. (2003). Speech emotion recognition using hidden Markov models. Speech Communication, 41, 603–623. CrossRef
Pillay, S. G., Ariyaeeinia, A., Pawlewski, M., & Sivakumaran, P. (2009). Speaker verification under mismatched data conditions. IET Signal Processing, 3(4), 236–246. CrossRef
Pitsikalis, V., & Maragos, P. (2009). Analysis and classification of speech signals by generalized fractal dimension features. Speech Communication, 51(12), 1206–1223. CrossRef
Pittermann, J., Pittermann, A., & Minker, W. (2010). Emotion recognition and adaptation in spoken dialogue systems. International Journal of Speech Technology, 13, 49–60. CrossRef
Polzin, T. S., & Waibel, A. H., (1998). Detecting emotions in speech. Cooperative multimodal communication. In second international conference 1998, CMC 1998.
Reynolds, D. A. (1995). Automatic speaker recognition using Gaussian mixture speaker models. The Lincoln Laboratory Journal, 8(2), 173–192.
Reynolds, D. A. (2002). An overview of automatic speaker recognition technology. ICASSP 2002, 4, IV-4072–IV-4075.
Reynolds, D. A., Quatieri, T. F., & Dunn, R. B., (2000). Speaker verification using adapted Gaussian mixture models. Digital Signal Processing, 10(1–3), 19–41. CrossRef
Scherer, K. R., Johnstone, T., Klasmeyer, G., & Banziger, T. (2000). Can automatic speaker verification be improved by training the algorithms on emotional speech? Proceedings of International Conference on Spoken Language Processing, 2, 807–810. CrossRef
Shahin, I. (2009). Verifying speakers in emotional environments. In The 9th IEEE international symposium on signal processing and information technology, Ajman, United Arab Emirates, December 2009, (pp. 328–333).
Shahin, I. (2013b). Speaker identification in emotional talking environments based on CSPHMM2s. Engineering Applications of Artificial Intelligence, 26, 1652–1659. https://doi.org/10.1016/j.engappai.2013.03.013. CrossRef
Shahin, I. (2014). Novel third-order hidden Markov models for speaker identification in shouted talking environments. Engineering Applications of Artificial Intelligence, 35, 316–323. https://doi.org/10.1016/j.engappai.2014.07.006. CrossRef
Ververidis, D., & Kotropoulos, C. (2006). Emotional speech recognition: Resources, features, and methods. Speech Communication, 48(9), 1162–1181. CrossRef
Vogt, T., & Andre, E., (2006). Improving automatic emotion recognition from speech via gender differentiation. In Proceedings of Language Resources and Evaluation Conference (LREC 2006), Genoa, Italy, 2006.
Wang, L., Wang, J., Li, L., Zheng, T. F., & Soong, F. K. (2016). Improving speaker verification performance against long-term speaker variability. Speech Communication, 79, 14–29. CrossRef
Wu, W., Zheng, T. F., Xu, M. X., & Bao, H. J., (2006). Study on speaker verification on emotional speech. In Proceedings of International Conference on Spoken Language Processing, INTERSPEECH 2006. September 2006, (pp. 2102–2105).
Yegnanarayana, B., Prasanna, S. R. M., Zachariah, J. M., & Gupta, C. S. (2005). Combining evidence from source, suprasegmental and spectral features for a fixed-text speaker verification systems. IEEE Transactions on Speech and Audio Processing, 13(4), 575–582. CrossRef
Zhou, G., Hansen, J. H. L., & Kaiser, J. F. (2001). Nonlinear feature based classification of speech under stress. IEEE Transactions on Speech & Audio Processing, 9(3), 201–216. CrossRef
- Three-stage speaker verification architecture in emotional talking environments
Ali Bou Nassif
- Springer US
Neuer Inhalt/© Filograph | Getty Images | iStock