Comparison of standard and hybrid modeling techniques for distributed speech recognition
Tóm tắt
Distributed speech recognition (DSR) is an interesting technology for mobile recognition tasks where the recognizer is split up into two parts and connected by a transmission channel. We compare the performance of standard and hybrid modeling approaches in this environment. The evaluation is done on clean and noisy speech samples taken from the TI digits and the Aurora databases. Our results show that, for this task, the hybrid modeling techniques can outperform standard continuous systems.
Từ khóa
#Speech recognition #Hidden Markov models #Bit rate #Vector quantization #Bandwidth #Channel coding #Computer science #Mobile computing #Working environment noise #DatabasesTài liệu tham khảo
cristoph, 1997, Advanced Training Methods and New Network Topologies for Hybrid MMI-Connectionist/HMM Speech Recognition Systems, IEEE Int Conf Acoust Speech an Signal Process (ICASSP), 3257
gerhard, 1994, Maximum Mutual Information Neural Networks for Hybrid Connectionist-HMM Speech Recognition Systems, IEEE Trans on Speech and Audio Processing Special Issue on Neural Networks for Speech, 2, 175, 10.1109/89.260360
jörg, 2000, Tied Posteriors; An Approach for Effective Introduction of Context Dependency in Hybrid NN/HMM LVCSR, IEEE Int Conf Acoust Speech an Signal Process (ICASSP)
hirsch, 2000, The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions, ISCA ITRW ASR2000
10.1109/89.326616
2000, Speech Processing, Transmission and Quality aspects (STQ); Distributed speech recognition; Front-end feature extraction algorithm; Compression algorithms, ETSI ES 201 108 v1 1 1 (2000–02)
10.1007/978-3-322-92767-5