Abstract
This paper presents a systematic study of performance of TempoRAl Patterns (TRAP) based features and their proposed modifications and combinations for speech recognition in noisy environment. The experimental results are obtained on AURORA 2 database with clean training data. We observed large dependency of performance of different TRAP modifications on noise level. Earlier proposed TRAP system modifications help in clean conditions but degrade the system performance in presence of noise. The combination techniques on the other hand can bring large improvement in case of weak noise and degrade only slightly for strong noise cases. The vector concatenation combination technique is improving the system performance up to strong noise.
This work was partly supported by European projects Caretaker (FP6-027231), by Grant Agency of Czech Republic under project No. 102/05/0278 and by Czech Ministry of Education under project No. MSM0021630528. The hardware used in this work was partially provided by CESNET under projects No. 119/2004, No. 162/2005 and No. 201/2006.
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References
Chen, B., Zhu, Q., Morgan, N.: Learning long-term temporal features in LVCSR using neural networks. In: Proc. ICSLP 2004, Jeju Island, KR (2004)
Jain, P., Hermansky, H., Kingsbury, B.: Distributed speech recognition using noise-robust MFCC and TRAPS-estimated manner features. In: Proc. of ICSLP 2002, Denver, Colorado, USA (2002)
Adami, A., Burget, L., Dupont, S., Garudadri, H., Grezl, F., Hermansky, H., Jain, P., Kajarekar, S., Morgan, N., Sivadas, S.: Qualcomm-ICSI-OGI features for ASR. In: Proc. ICSLP 2002, Denver, Colorado, USA (2002)
Jain, P., Hermansky, H.: Beyond a single critical-band in TRAP based ASR. In: Proc. Eurospeech 2003, Geneva, Switzerland, pp. 437–440 (2003)
Grézl, F., Hermansky, H.: Local averaging and differentiating of spectral plane for TRAP-based ASR. In: Proc. Eurospeech 2003, Geneva, Switzerland (2003)
Grézl, F.: Combinations of TRAP-based systems. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 323–330. Springer, Heidelberg (2004)
Pearce, D.: Enabling new speech driven servicesfor mobile devices: An overview of the ETSIstandards activities for distributed speech recognition front-ends. In: Applied Voice Input/Output Society Conference (AVIOS 2000), San Jose, CA (2000)
Cole, R., Noel, M., Lander, T., Durham, T.: New telephone speech corpora at CSLU. In: Proc. of EUROSPEECH 1995, Madrid, Spain, pp. 821–824 (1995)
Misra, H., Bourlard, H., Tyagi, V.: New entropy based combination rules in HMM/ANN multi-stream asr. In: Proc. ICASSP 2003, Hong Kong, China (2003)
Grézl, F.: Local time-frequency operators in TRAPs for speech recognition. In: Matoušek, V., Mautner, P. (eds.) TSD 2003. LNCS (LNAI), vol. 2807, pp. 269–274. Springer, Heidelberg (2003)
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Grézl, F., Černocký, J. (2007). TRAP-Based Techniques for Recognition of Noisy Speech. In: Matoušek, V., Mautner, P. (eds) Text, Speech and Dialogue. TSD 2007. Lecture Notes in Computer Science(), vol 4629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74628-7_36
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DOI: https://doi.org/10.1007/978-3-540-74628-7_36
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