When an automatic speech recognition (ASR) system is deployed for real-world applications, it often receives only one utterance at a time for decoding. This single utterance could be of short duration depending on the ASR task. In these cases, robust estimation of speaker normalizing methods like feature-space maximum likelihood linear regression (FMLLR) and i-vectors may not be feasible. In this paper, we propose two unsupervised speaker normalization techniques-one at feature level and other at model level of acoustic modeling-to overcome the drawbacks of FMLLR and i-vectors in real-time scenarios. At feature level, we propose the use of deep neural networks (DNN) to generate pseudo-FMLLR features from time-synchronous pair of filterbank and FMLLR features. These pseudo-FMLLR features can then be used for DNN acoustic model training and decoding. At model level, we propose a generalized distillation framework, where a teacher DNN trained on FMLLR features guides the training and optimization of a student DNN trained on filterbank features. In both the proposed methods, the ambiguity in choosing the speaker-specific FMLLR transform can be reduced by augmenting i-vectors to the input filterbank features. Experiments conducted on 33-h and 110-h subsets of Switchboard corpus show that the proposed methods provide significant gains over DNNs trained on FMLLR, i-vector appended FMLLR, filterbank and i -vector appended filterbank features, in real-time scenario.

One of the major problems in acoustic modeling for a low-resource language is data sparsity. In recent years, cross-lingual acoustic modeling techniques have been employed to overcome this problem. In this paper we propose multiple cross-lingual techniques to address the problem of data insufficiency. The first method, which we call as the cross-lingual phone-CAT, uses the principles of phone-cluster adaptive training (phone-CAT), where the parameters of context-dependent states are obtained by linear interpolation of monophone cluster models. The second method uses the interpolation vectors of phone-CAT, which is known to capture the phonetic context information, to map phonemes between two languages. Finally, the data-driven phoneme-mapping technique is incorporated into the cross-lingual phone-CAT, to obtain what we call as the phoneme-mapped cross-lingual phone-CAT. The proposed techniques are employed in acoustic modeling of three Indian languages namely Bengali, Hindi and Tamil. The phoneme-mapped cross-lingual phone-CAT gave relative improvements of 15.14% for Bengali, 16.4% for Hindi and 11.3% for Tamil over the conventional cross-lingual subspace Gaussian mixture model (SGMM) in low-resource scenario.