Jasha Droppo

Uncertainty decoding with SPLICE for noise robust speech recognition

Speech recognition front end noise removal algorithms have. in the past, estimated clean speech features from corrupted speech features. The accuracy of the noise removal process varies from frame to frame, and from dimension to dimension in the feature stream, due in part to the instantaneous SR of the input. In this paper, we show that localized knowledge of the accuracy of the noise removal process can be directly incorporated into the Gaussian evaluation within the decoder, to produce higher recognition accuracies. To prove this concept, we modify the SPLICE algorithm to output uncertainty information, and show that the combination of SPLICE with uncertainty decoding can remove 74.2% of the errors in a subset of the Aurora2 task.

Dynamic compensation of HMM variances using the feature enhancement uncertainty computed from a parametric model of speech distortion

This paper presents a new technique for dynamic, frame-by-frame compensation of the Gaussian variances in the hidden Markov model (HMM), exploiting the feature variance or uncertainty estimated during the speech feature enhancement process, to improve noise-robust speech recognition. The new technique provides an alternative to the Bayesian predictive classification decision rule by carrying out an integration over the feature space instead of over the model-parameter space, offering a much simpler system implementation, lower computational cost, and dynamic compensation capabilities at the frame level. The computation of the feature enhancement variances is carried out using a probabilistic and parametric model of speech distortion, free from the use of any stereo training data. Dynamic compensation of the Gaussian variances in the HMM recognizer is derived, which is simply enlarging the HMM Gaussian variances by the feature enhancement variances. Experimental evaluation using the full Aurora2 test data sets demonstrates a significant digit error rate reduction, averaged over all noisy and signal-to-noise-ratio conditions, compared with the baseline that did not exploit the enhancement variance information. When the true enhancement variances are used, further dramatic error rate reduction is observed, indicating the strong potential for the new technique and the strong need for high accuracy in estimating the variances associated with feature enhancement. All the results, using either the true variances of the enhanced features or the estimated ones, show that the greatest contribution to recognizers performance improvement is due to the use of the uncertainty for the static features, next due to the delta features, and the least due to the delta-delta features.

High-performance robust speech recognition using stereo training data

We describe a novel technique of SPLICE (Stereo-based Piecewise Linear Compensation for Environments) for high performance robust speech recognition. It is an efficient noise reduction and channel distortion compensation technique that makes effective use of stereo training data. We present a new version of SPLICE using the minimum-mean-square-error decision, and describe an extension by training clusters of hidden Markov models (HMMs) with SPLICE processing. Comprehensive results using a Wall Street Journal large vocabulary recognition task and with a wide range of noise types demonstrate the superior performance of the SPLICE technique over that under noisy matched conditions (19% word error rate reduction). The new technique is also shown to consistently outperform the spectral-subtraction noise reduction technique, and is currently being integrated into the Microsoft MiPad, a new generation PDA prototype.

Multi-task learning in deep neural networks for improved phoneme recognition

In this paper we demonstrate how to improve the performance of deep neural network (DNN) acoustic models using multi-task learning. In multi-task learning, the network is trained to perform both the primary classification task and one or more secondary tasks using a shared representation. The additional model parameters associated with the secondary tasks represent a very small increase in the number of trained parameters, and can be discarded at runtime. In this paper, we explore three natural choices for the secondary task: the phone label, the phone context, and the state context. We demonstrate that, even on a strong baseline, multi-task learning can provide a significant decrease in error rate. Using phone context, the phonetic error rate (PER) on TIMIT is reduced from 21.63% to 20.25% on the core test set, and surpassing the best performance in the literature for a DNN that uses a standard feed-forward network architecture.

Enhancement of log Mel power spectra of speech using a phase-sensitive model of the acoustic environment and sequential estimation of the corrupting noise

This paper presents a novel speech feature enhancement technique based on a probabilistic, nonlinear acoustic environment model that effectively incorporates the phase relationship (hence phase sensitive) between the clean speech and the corrupting noise in the acoustic distortion process. The core of the enhancement algorithm is the MMSE (minimum mean square error) estimator for the log Mel power spectra of clean speech based on the phase-sensitive environment model, using highly efficient single-point, second-order Taylor series expansion to approximate the joint probability of clean and noisy speech modeled as a multivariate Gaussian. Since a noise estimate is required by the MMSE estimator, a high-quality, sequential noise estimation algorithm is also developed and presented. Both the noise estimation and speech feature enhancement algorithms are evaluated on the Aurora2 task of connected digit recognition. Noise-robust speech recognition results demonstrate that the new acoustic environment model which takes into account the relative phase in speech and noise mixing is superior to the earlier environment model which discards the phase under otherwise identical experimental conditions. The results also show that the sequential MAP (maximum a posteriori) learning for noise estimation is better than the sequential ML (maximum likelihood) learning, both evaluated under the identical phase-sensitive MMSE enhancement condition.

Recursive estimation of nonstationary noise using iterative stochastic approximation for robust speech recognition

We describe a novel algorithm for recursive estimation of nonstationary acoustic noise which corrupts clean speech, and a successful application of the algorithm in the speech feature enhancement framework of noise-normalized SPLICE for robust speech recognition. The noise estimation algorithm makes use of a nonlinear model of the acoustic environment in the cepstral domain. Central to the algorithm is the innovative iterative stochastic approximation technique that improves piecewise linear approximation to the nonlinearity involved and that subsequently increases the accuracy for noise estimation. We report comprehensive experiments on SPLICE-based, noise-robust speech recognition for the AURORA2 task using the results of iterative stochastic approximation. The effectiveness of the new technique is demonstrated in comparison with a more traditional, MMSE noise estimation algorithm under otherwise identical conditions. The word error rate reduction achieved by iterative stochastic approximation for recursive noise estimation in the framework of noise-normalized SPLICE is 27.9% for the multicondition training mode, and 67.4% for the clean-only training mode, respectively, compared with the results using the standard cepstra with no speech enhancement and using the baseline HMM supplied by AURORA2. These represent the best performance in the clean-training category of the September-2001 AURORA2 evaluation. The relative error rate reduction achieved by using the same noise estimate is increased to 48.40% and 76.86%, respectively, for the two training modes after using a better designed HMM system. The experimental results demonstrated the crucial importance of using the newly introduced iterations in improving the earlier stochastic approximation technique, and showed sensitivity of the noise estimation algorithms performance to the forgetting factor embedded in the algorithm.

Estimating cepstrum of speech under the presence of noise using a joint prior of static and dynamic features

In this paper, we present a new algorithm for statistical speech feature enhancement in the cepstral domain. The algorithm exploits joint prior distributions (in the form of Gaussian mixture) in the clean speech model, which incorporate both the static and frame-differential dynamic cepstral parameters. Full posterior probabilities for clean speech given the noisy observation are computed using a linearized version of a nonlinear acoustic distortion model, and, based on this linear approximation, the conditional minimum mean square error (MMSE) estimator for the clean speech feature is derived rigorously using the full posterior. The final form of the derived conditional MMSE estimator is shown to be a weighted sum of three separate terms, and the sum is weighted again by the posterior for each of the mixture component in the speech model. The first of the three terms is shown to arrive naturally from the predictive mechanism embedded in the acoustic distortion model in absence of any prior information. The remaining two terms result from the speech model using only the static prior and only the dynamic prior, respectively. Comprehensive experiments are carried out using the Aurora2 database to evaluate the new algorithm. The results demonstrate significant improvement in noise-robust recognition accuracy by incorporating the joint prior for both static and dynamic parameter distributions in the speech model, compared with using only the static or dynamic prior and with using no prior.

The microsoft 2016 conversational speech recognition system

We describe Microsofts conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task.

Air- and bone-conductive integrated microphones for robust speech detection and enhancement

We present a novel hardware device that combines a regular microphone with a bone-conductive microphone. The device looks like a regular headset and it can be plugged into any machine with a USB port. The bone-conductive microphone has an interesting property: it is insensitive to ambient noise and captures the low frequency portion of the speech signals. Thanks to the signals from the bone-conductive microphone, we are able to detect very robustly whether the speaker is talking, eliminating more than 90% of background speech. Furthermore, by combining both channels, we are able to remove background speech significantly, even when the background speaker speaks at the same time as the speaker wearing the headset.

Noise Adaptive Training for Robust Automatic Speech Recognition

In traditional methods for noise robust automatic speech recognition, the acoustic models are typically trained using clean speech or using multi-condition data that is processed by the same feature enhancement algorithm expected to be used in decoding. In this paper, we propose a noise adaptive training (NAT) algorithm that can be applied to all training data that normalizes the environmental distortion as part of the model training. In contrast to feature enhancement methods, NAT estimates the underlying “pseudo-clean” model parameters directly without relying on point estimates of the clean speech features as an intermediate step. The pseudo-clean model parameters learned with NAT are later used with vector Taylor series (VTS) model adaptation for decoding noisy utterances at test time. Experiments performed on the Aurora 2 and Aurora 3 tasks demonstrate that the proposed NAT method obtain relative improvements of 18.83% and 32.02%, respectively, over VTS model adaptation.