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Conference Paper
A Useful Feature-Engineering Approach for a LVCSR System Based on CD-DNN-HMM Algorithm
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- Authors
- Sung Joo Lee, Byung Ok Kang, Hoon Chung, Jeon Gue Park
- Issue Date
- 2015-08
- Citation
- European Signal Processing Conference (EUSIPCO) 2015, pp.1436-1440
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we propose a useful feature-engineering approach for Context-Dependent Deep-Neural-Network Hidden-Markov-Model (CD-DNN-HMM) based Large-Vocabulary-Continuous-Speech-Recognition (LVCSR) systems. The speech recognition performance of a LVCSR system is improved from two feature-engineering perspectives. The first performance improvement is achieved by adopting the intra/inter-frame feature subsets when the Gaussian-Mixture-Model (GMM) HMMs for the HMM state-level alignment are built. And the second performance gain is then followed with the additional features augmenting the front-end of the DNN. We evaluate the effectiveness of our feature-engineering approach under a series of Korean speech recognition tasks (isolated single-syllable recognition with a medium-sized speech corpus and conversational speech recognition with a large-sized database) using the Kaldi speech recognition toolkit. The results show that the proposed featureengineering approach outperforms the traditional Mel Frequency Cepstral Coefficient (MFCCs) GMM + Melfrequency filter-bank output DNN method.
- KSP Keywords
- CD-DNN-HMM, Conversational speech recognition, Engineering approach, Front-End, Inter-frame, Korean speech, Markov model, Mel-Frequency Cepstrum Coefficients(MFCC), Performance gain, Speech corpus, cepstral coefficients