Neural nets and hidden Markov models: review and generalizations
Speech Communication - Eurospeech '91
Computer lipreading for improved accuracy in automatic speech recognition
Computer lipreading for improved accuracy in automatic speech recognition
Speech Communication - Special issue on speech processing in adverse conditions
Speech recognition: theory and C++ implementation
Speech recognition: theory and C++ implementation
Broadcast News Transcription Using HTK
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Using multiple acoustic feature sets for speech recognition
Speech Communication
ACM Transactions on Asian Language Information Processing (TALIP)
Pattern Recognition Letters
Invited paper: Automatic speech recognition: History, methods and challenges
Pattern Recognition
Linear discriminant analysis for improved large vocabulary continuous speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Application of prosody models for developing speech systems in Indian languages
International Journal of Speech Technology
Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)
International Journal of Speech Technology
International Journal of Speech Technology
Combining Spectral Representations for Large-Vocabulary Continuous Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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Despite the significant progress of automatic speech recognition (ASR) in the past three decades, it could not gain the level of human performance, particularly in the adverse conditions. To improve the performance of ASR, various approaches have been studied, which differ in feature extraction method, classification method, and training algorithms. Different approaches often utilize complementary information; therefore, to use their combination can be a better option. In this paper, we have proposed a novel approach to use the best characteristics of conventional, hybrid and segmental HMM by integrating them with the help of ROVER system combination technique. In the proposed framework, three different recognizers are created and combined, each having its own feature set and classification technique. For design and development of the complete system, three separate acoustic models are used with three different feature sets and two language models. Experimental result shows that word error rate (WER) can be reduced about 4% using the proposed technique as compared to conventional methods. Various modules are implemented and tested for Hindi Language ASR, in typical field conditions as well as in noisy environment.