Arnab Ghoshal

The subspace Gaussian mixture model-A structured model for speech recognition

We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.

Subspace Gaussian Mixture Models for speech recognition

We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define the subspace. This style of acoustic model allows for a much more compact representation and gives better results than a conventional modeling approach, particularly with smaller amounts of training data.

Multilingual training of deep neural networks

We investigate multilingual modeling in the context of a deep neural network (DNN) - hidden Markov model (HMM) hybrid, where the DNN outputs are used as the HMM state likelihoods. By viewing neural networks as a cascade of feature extractors followed by a logistic regression classifier, we hypothesise that the hidden layers, which act as feature extractors, will be transferable between languages. As a corollary, we propose that training the hidden layers on multiple languages makes them more suitable for such cross-lingual transfer. We experimentally confirm these hypotheses on the GlobalPhone corpus using seven languages from three different language families: Germanic, Romance, and Slavic. The experiments demonstrate substantial improvements over a monolingual DNN-HMM hybrid baseline, and hint at avenues of further exploration.

Hybrid acoustic models for distant and multichannel large vocabulary speech recognition

We investigate the application of deep neural network (DNN)-hidden Markov model (HMM) hybrid acoustic models for far-field speech recognition of meetings recorded using microphone arrays. We show that the hybrid models achieve significantly better accuracy than conventional systems based on Gaussian mixture models (GMMs). We observe up to 8% absolute word error rate (WER) reduction from a discriminatively trained GMM baseline when using a single distant microphone, and between 4-6% absolute WER reduction when using beamforming on various combinations of array channels. By training the networks on audio from multiple channels, we find the networks can recover significant part of accuracy difference between the single distant microphone and beamformed configurations. Finally, we show that the accuracy of a network recognising speech from a single distant microphone can approach that of a multi-microphone setup by training with data from other microphones.

Convolutional Neural Networks for Distant Speech Recognition

We investigate convolutional neural networks (CNNs) for large vocabulary distant speech recognition, trained using speech recorded from a single distant microphone (SDM) and multiple distant microphones (MDM). In the MDM case we explore a beamformed signal input representation compared with the direct use of multiple acoustic channels as a parallel input to the CNN. We have explored different weight sharing approaches, and propose a channel-wise convolution with two-way pooling. Our experiments, using the AMI meeting corpus, found that CNNs improve the word error rate (WER) by 6.5% relative compared to conventional deep neural network (DNN) models and 15.7% over a discriminatively trained Gaussian mixture model (GMM) baseline. For cross-channel CNN training, the WER improves by 3.5% relative over the comparable DNN structure. Compared with the best beamformed GMM system, cross-channel convolution reduces the WER by 9.7% relative, and matches the accuracy of a beamformed DNN.

Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR

We investigate the use of cross-lingual acoustic data to initialise deep neural network (DNN) acoustic models by means of unsupervised restricted Boltzmann machine (RBM) pre-training. DNNs for German are pretrained using one or all of German, Portuguese, Spanish and Swedish. The DNNs are used in a tandem configuration, where the network outputs are used as features for a hidden Markov model (HMM) whose emission densities are modeled by Gaussian mixture models (GMMs), as well as in a hybrid configuration, where the network outputs are used as the HMM state likelihoods. The experiments show that unsupervised pretraining is more crucial for the hybrid setups, particularly with limited amounts of transcribed training data. More importantly, unsupervised pretraining is shown to be language-independent.

Generating exact lattices in the WFST framework

We describe a lattice generation method that is exact, i.e. it satisfies all the natural properties we would want from a lattice of alternative transcriptions of an utterance. This method does not introduce substantial overhead above one-best decoding. Our method is most directly applicable when using WFST decoders where the WFST is “fully expanded”, i.e. where the arcs correspond to HMM transitions. It outputs lattices that include HMM-state-level alignments as well as word labels. The general idea is to create a state-level lattice during decoding, and to do a special form of determinization that retains only the best-scoring path for each word sequence. This special determinization algorithm is a solution to the following problem: Given a WFST A, compute a WFST B that, for each input-symbol-sequence of A, contains just the lowest-cost path through A.

Revisiting hybrid and GMM-HMM system combination techniques

In this paper we investigate techniques to combine hybrid HMM-DNN (hidden Markov model - deep neural network) and tandem HMM-GMM (hidden Markov model - Gaussian mixture model) acoustic models using: (1) model averaging, and (2) lattice combination with Minimum Bayes Risk decoding. We have performed experiments on the “TED Talks” task following the protocol of the IWSLT-2012 evaluation. Our experimental results suggest that DNN-based and GMM-based acoustic models are complementary, with error rates being reduced by up to 8% relative when the DNN and GMM systems are combined at model-level in a multi-pass automatic speech recognition (ASR) system. Additionally, further gains were obtained by combining model-averaged lattices with the one obtained from baseline systems.

Acoustic data-driven pronunciation lexicon for large vocabulary speech recognition

Speech recognition systems normally use handcrafted pronunciation lexicons designed by linguistic experts. Building and maintaining such a lexicon is expensive and time consuming. This paper concerns automatically learning a pronunciation lexicon for speech recognition. We assume the availability of a small seed lexicon and then learn the pronunciations of new words directly from speech that is transcribed at word-level. We present two implementations for refining the putative pronunciations of new words based on acoustic evidence. The first one is an expectation maximization (EM) algorithm based on weighted finite state transducers (WFSTs) and the other is its Viterbi approximation. We carried out experiments on the Switchboard corpus of conversational telephone speech. The expert lexicon has a size of more than 30,000 words, from which we randomly selected 5,000 words to form the seed lexicon. By using the proposed lexicon learning method, we have significantly improved the accuracy compared with a lexicon learned using a grapheme-to-phoneme transformation, and have obtained a word error rate that approaches that achieved using a fully handcrafted lexicon.

Regularized subspace Gaussian mixture models for cross-lingual speech recognition

We investigate cross-lingual acoustic modelling for low resource languages using the subspace Gaussian mixture model (SGMM). We assume the presence of acoustic models trained on multiple source languages, and use the global subspace parameters from those models for improved modelling in a target language with limited amounts of transcribed speech. Experiments on the GlobalPhone corpus using Spanish, Portuguese, and Swedish as source languages and German as target language (with 1 hour and 5 hours of transcribed audio) show that multilingually trained SGMM shared parameters result in lower word error rates (WERs) than using those from a single source language. We also show that regularizing the estimation of the SGMM state vectors by penalizing their ℓ1-norm help to overcome numerical instabilities and lead to lower WER.