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 + Mel-frequency filter-bank output DNN method.
['Sung Joo Lee', 'Byung Ok Kang', 'Hoon Chung', 'Jeon Gue Park']
A useful feature-engineering approach for a LVCSR system based on CD-DNN-HMM algorithm