Spectral feature mapping with mimic loss for robust speech recognition

03/26/2018 ∙ by Deblin Bagchi, et al. ∙ 0

For the task of speech enhancement, local learning objectives are agnostic to phonetic structures helpful for speech recognition. We propose to add a global criterion to ensure de-noised speech is useful for downstream tasks like ASR. We first train a spectral classifier on clean speech to predict senone labels. Then, the spectral classifier is joined with our speech enhancer as a noisy speech recognizer. This model is taught to imitate the output of the spectral classifier alone on clean speech. This mimic loss is combined with the traditional local criterion to train the speech enhancer to produce de-noised speech. Feeding the de-noised speech to an off-the-shelf Kaldi training recipe for the CHiME-2 corpus shows significant improvements in WER.



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

Automatic Speech Recognition (ASR) has shown tremendous progress over the years in recognizing clean speech. However, traditional DNN-HMM ASR systems still suffer from performance degradation in the presence of acoustic interference, such as additive noise and room reverberation. Some strategies for building a noise-robust speech recognizer include using noise-invariant features [1], augmented data [2], bulkier acoustic models like LSTMs and CNNs [2, 3] and sophisticated language models [2, 3]. Few groups, however, have looked at systems that train only a speech enhancement model, which can be used for different tasks.


Clean speech



Senone label


Noisy speech



Clean speech


Noisy speech








Figure 1: Our speech enhancement system is trained in three steps. (1) The spectral classifier is trained to predict senone labels with cross-entropy criterion (classification loss, ). (2) The spectral mapper is pre-trained to map from noisy speech to clean speech using MSE criterion (fidelity loss, ). (3) The spectral mapper is trained using joint loss from both the clean speech and the outputs of the classifier when fed clean speech (mimic loss, ). The gray models have frozen weights.

A speech enhancement front-end refers to a performance-boosting denoising technique that can be attached to any standard automatic speech recognition model. Some deep learning models for speech enhancement attempt to estimate an ideal ratio mask (IRM) for removing noise from a speech signal

[4]. Others utilize spectral mapping in the signal domain [5, 6] or in the feature domain [7, 8] to directly predict features.

Recent work in computer vision has seen notable success in addressing the problem of poor resolution in modified input data, using a framework broadly referred to as Generative Adversarial Networks (GANs). Fundamentally, these gains are achieved by the injection of auxiliary or proxy learning objectives into more traditional pipelines. These auxiliary objectives exploit an adversarial relationship between two neural networks (a

generator and a discriminator) to find a Nash equilibrium in which generating sensible data is the optimal behavior for the generator [9]. This has the effect of refining the distribution of the generated data closer to some desirable outcome relative to a system trained without the auxiliary objective, e.g., sharper, more realistic generated images.

In light of these successes, in particular that of [9], inserting an auxiliary realism objective into a speech denoising pipeline seems to be a natural avenue to pursue improved performance. However, initial experiments with GANs conditioned on noisy speech inputs, in which the discriminator network was trained to distinguish between real and fake clean/noisy input pairs, exposed the well-known mode collapse problem endemic to GANs [10]. Results failed to improve upon simple baselines of noisy-to-clean speech mappings trained with only MSE loss. Other attempts have also failed to improve upon a DNN baseline, as in [11]. We speculate that the difference in performance from experiments in the visual domain may be due to the relatively non-local structure of the speech signal in the frequency axis (i.e. harmonic structure), as well as the smoothness of speech features as compared to images.

The work described in this paper is motivated by the observations of over-simple outputs from GANs that seemed to be stuck in mode collapse orbits. We hypothesize that a training objective that can provide stronger feedback than a simple real or fake determination will be better able to guide the parameters of the denoising network towards producing output that behaves like actual speech. While our resulting system retains none of the properties of being generative or trained adversarially, the insight to use an auxiliary task to improve the denoising process is drawn from that body of work.

The auxiliary global objective that we add to our local criterion is the behavioral loss of a classifier trained on clean speech. We call this additional objective the mimic loss. First, we train a senone classifier using clean speech as input, and a spectral mapper network [8, 6] using parallel noisy and clean speech frame pairs. Next, we freeze the weights of the acoustic model and join our pre-trained spectral mapper to it. We then pass noisy speech frames to train our spectral mapper with a joint objective, i.e. a weighted sum of the traditional fidelity loss and mimic loss. The mimic loss, then, is MSE loss with respect to the softmax (or pre-softmax) outputs of the classifier fed with the corresponding clean speech frame. See figure 1 for a graphical depiction of the model.

This technique of using one model to teach another was proposed by Ba and Caruna [12] for the task of model compression. In their work, they introduce student-teacher learning, where separate teacher and student models are trained to do the same task. Mimic loss, on the other hand, is used to train the student model to do a different task from the teacher model.

2 prior work

To deal with noise, many DNN based methods have been proposed to improve the robustness of ASR systems. In acoustic modeling, using Convolutional Neural Networks (CNNs), such as in


and Long Short Term Memory Networks (LSTMs) in

[14] has resulted in an improvement in performance. LSTMs have also been successfully used as speech enhancement front-ends in [15, 14].

Spectral mapping has been used to generate clean speech signals. However, in [8, 7] they use only a local learning objective. Student-teacher networks have been used to improve the quality of noisy speech recognition [16, 17, 18]. Our model uses mimic loss instead of student-teacher learning, which means the speech enhancer is not jointly trained with a particular acoustic model. This speech enhancer could be used as a pre-processor for any ASR system, or for another similar dataset. This modularity is the strength of mimic loss.

3 system description

In this section, we will describe the major components of our system: namely, the spectral mapper, the spectral classifier, and the overall framework binding the two together.

3.1 Spectral mapping

Spectral mapping improves performance of the speech recognizer by learning a mapping from noisy spectral patterns to clean features. We train a DNN-based spectral mapper for feature denoising. In our previous work [7, 8], we have shown that a DNN-based spectral mapper, which takes noisy spectrogram as input to predict clean filterbank features for ASR, yields good results on the CHiME-2 noisy and reverberant dataset.

Specifically, we first divide the input time-domain signals into 25-ms frames with a 10-ms frame shift, and then apply short time Fourier transform (STFT) to compute log spectral magnitudes in each time frame. For a 16 kHz signal, each frame contains 400 samples, and we use 512-point Fourier transform to compute the magnitudes, forming a 257-dimensional log magnitude vector. Each noisy spectral component

for frequency at time slice is augmented on the input by the deltas and double deltas, as well as a five frame window (designated ]), leading to the dimensionality of being . Similarly, we define to be the clean spectral slice at time .

We then use a feed-forward neural network

to map noisy spectral slices to clean spectral features

using an MSE loss function, which we call

fidelity loss.


3.2 Spectral classifier

The spectral classifier is similar to the traditional DNN acoustic model trained to classify a stacked clean spectral pattern to its corresponding senone class . We train the classifier using a cross entropy criterion; critically, once the classifier is trained, we freeze the weights as a model of appropriate behavior under clean speech.

3.3 Joint loss

We can define the mimic loss as the mean square difference between a -dimensional representation within the spectral classifier evaluated on clean speech and its paired cleaned speech :


We experimented with two different representations for : the posterior output of the senones after softmax normalization (post-softmax) and the layer outputs prior to the softmax normalization (pre-softmax).

While training the speech enhancer, we found that using only mimic loss was not enough to allow the model to converge. We speculate that the task of predicting senones is too different from the task of predicting clean speech for the error signal to drive the output of the speech enhancer to actually look like speech. Combining the fidelity and mimic losses into a joint loss allows the enhancer to better imitate the behavior of the classifier under clean speech while keeping the projection of noisy speech closer to clean speech.


The hyper-parameter is used to ensure that both of the losses that make up the joint loss have a similar magnitude. We used 0.1 for pre-softmax and 1000 for post-softmax.

4 experimental setup

We evaluate the effectiveness of our proposed method on Track 2 of the CHiME-2 challenge [19], which is a medium-vocabulary task for word recognition under reverberant and noisy environments without speaker movements. In this task, three types of data are provided based on the Wall Street Journal (WSJ0) 5k vocabulary read speech corpus: clean, reverberant and reverberant+noisy. The clean utterances are extracted from the WSJ0 database. The reverberant utterances are created by convolving the clean speech with binaural room impulse responses (BRIR) corresponding to a frontal position in a family living room. Real-world non-stationary noise background recorded in the same room is mixed with the reverberant utterances to form the reverberant+noisy set. The noise excerpts are selected such that the signal-to-noise ratio (SNR) ranges among -6, -3, 0, 3, 6 and 9 dB without scaling. The multi-condition training, development and test sets of the reverberant+noisy set contain 7138, 2454 and 1980 utterances respectively, which are the same utterances in the clean set but with reverberation and noise at 6 different SNR conditions.

Our system is monaural. In our experiments, we simply average the signals from the left and right ear. A GMM-HMM system is built using the Kaldi toolkit [20] on the clean utterances in the WSJ0-5k to get the senone state for each frame of the corresponding noisy-reverberant utterances. The initial clean alignments are obtained by performing forced alignment on the clean utterances. To refine the initial clean alignments, we further trained a DNN-based acoustic model using the filterbank features of the clean utterances, and re-generate clean alignments. These clean alignments are used as the labels for training all the acoustic models in this study. Note that the DNN-HMM hybrid system built on the clean utterances is a powerful recognizer. It achieves 2.3% WER on the clean test set of the WSJ0-5k dataset.

px Spectral input to Kaldi CE WER sMBR WER No enhancement 18.0 17.3 Enhancement via fidelity loss 17.5 16.5 Enhancement via joint loss   w/ post-softmax mimic loss 16.5 15.7   w/ pre-softmax mimic loss 15.7 14.7

Table 1: Experimental results on the CHiME2 test set; CE WER is the word error rate of a DNN-HMM hybrid system trained using a cross-entropy criterion. sMBR WER is the error rate after sequential minimum Bayes risk training.

px Enhancement -6 dB -3 dB 0 dB 3 dB 6 dB 9 dB None 29.8 22.3 17.6 13.4 10.9 9.7 Fidelity loss 29.3 20.6 16.2 12.4 10.9 9.2 Joint loss 25.9 19.6 14.8 10.9 9.0 8.0 px

Table 2: Experimental results on the CHiME2 test set, broken down across different SNRs. Mimic loss based on pre-softmax units. Bold indicates the best performing system for each evaluation subset.
Study WER
Wang et.al [1] 10.6
Weninger et.al[15] 13.8
proposed approach 14.7
Narayanan-Wang[4] 15.4
Chen et. al [14] 16.0
Table 3: Performance comparison with other studies on the CHiME2 test set.

4.1 Description of the acoustic model

In order to determine the effectiveness of the additional criterion, we train a model using the denoised features with an off-the-shelf Kaldi recipe. The DNN-HMM hybrid system is trained using the clean WSJ0-5k alignments generated using the method stated above. The DNN has 7 hidden layers, with 2048 sigmoid neurons in each layer and a softmax output layer. Splicing context size for the filter-bank features was fixed at 11 frames, with the minibatch-size being 1024. After that, we train the DNN with sMBR sequence training to achieve better performance. We regenerate the lattices after the first iteration and train for 4 more iterations. We use the CMU pronunciation dictionary and the official 5k close-vocabulary tri-gram language model in our experiments.

4.2 Description of the spectral classifier

The spectral classifier network is a multilayer feed-forward network which classifies clean speech frames as one of 1999 senone labels. We use 6 layers of 1024 neurons with Leaky ReLU activations and batch normalization between all the layers. While training the spectral classifier on clean speech, we apply softmax after the topmost layer, and use a cross entropy criterion to teach the network to produce senones.

4.3 Description of the spectral mapper

The spectral mapper is composed of a simple two-layer feed-forward network with 2048 neurons in each layer. The fact that this model is simple means it can be used in some low-resource situations, and is fast to use. Note that mimic loss can be applied to improve the results of any kind of speech enhancer. To regularize the network, we use batch normalization and a dropout rate of 0.5 between every layer. We also use ReLU activations after each layer. The classifier and mapper are written using TensorFlow

111Code at https://github.com/OSU-slatelab/actor_critic_specmap.

4.4 Results

We see in Table 1 that the outputs of the recognizer before the softmax provide a better target for the noisy speech recognizer, as suggested in [12], even though our setup is quite different from theirs. The difference in performance between pre- and post-softmax targets may be due to a mismatch between target domain and loss criterion; ongoing work suggests that a cross-entropy mimic loss on post-softmax targets performs similarly to MSE on pre-softmax. Furthermore, the information loss in the softmax normalization may “broaden” the allowable spectral mappings, harming generalization. This suggests that it is helpful for the noisy recognizer not only to have the same targets as the clean recognizer, but also to learn to behave in the same way as the clean recognizer.

We also demonstrate this fact by training the noisy speech recognizer using hard targets rather than the soft targets of mimic loss. This caused the joint loss in the spectral mapper to diverge, providing more evidence that the noisy speech recognizer must learn to mimic the behavior, not just the targets of the clean speech recognizer. Finally, in Table 2 we break down our results into different SNRs and see that the gains are consistent over all Signal-to-Noise levels.

4.5 Comparison with other systems

For context, we show in Table 3 the performance of our system relative to other published results in the field. The better-performing models in this list use more sophisticated models (like RNNs and LSTMs) for front-end speech enhancement [14, 15] and acoustic modeling [14], as well as noise-invariant features [1] (e.g., PNCC, MRCG). We use an off-the-shelf Kaldi recipe using DNN-HMM to do speech recognition, as well as a simple 2 layer feed-forward network to do spectral mapping. Again, mimic loss can theoretically be used to improve the results of any front-end system.

5 conclusion

We have proposed a speech enhancement criterion, called mimic loss, which can be used to produce speech that is useful for downstream ASR tasks. The mimic loss comes from comparing the outputs of a frozen clean speech recognizer, before softmax is applied, on clean and enhanced speech. This configuration allows the speech enhancement output to be used as clean speech for any downstream task. We see that with mimic loss, the spectral mapper learns to produce more detailed speech data, retaining features that fidelity loss alone fails to model. Mimicking the behavior of the pre-softmax layer of the classifier was superior to mimicking the output of the senone posterior estimates; in general, the lower error rates show that these features are helpful for downstream tasks.

In future work, we propose to extend this work by matching every layer of the phone recognizer, rather than just the inputs and outputs. We could also use a variety of models for speech enhancement to demonstrate the effectiveness of mimic loss. Another avenue is to evaluate the output of our system in multiple domains to determine the effectiveness of this approach at learning domain-invariant representations of speech. Finally, we can train a more sophisticated acoustic model, rather than using an off-the-shelf Kaldi recipe.

6 Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant IIS-1409431. We also thank the Ohio Supercomputer Center (OSC) [21] for providing us with computational resources.


  • [1] Z.-Q. Wang and D. Wang, “A joint training framework for robust automatic speech recognition,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 24, no. 4, pp. 796–806, 2016.
  • [2] J. Du, Y.-H. Tu, L. Sun, F. Ma, H.-K. Wang, J. Pan, C. Liu, J.-D. Chen, and C.-H. Lee, “The USTC-iFlytek system for CHiME-4 challenge,” Proc. CHiME, pp. 36–38, 2016.
  • [3] T. Yoshioka, N. Ito, M. Delcroix, A. Ogawa, K. Kinoshita, M. Fujimoto, C. Yu, W. J. Fabian, M. Espi, T. Higuchi, et al., “The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices,” in Automatic Speech Recognition and Understanding (ASRU), 2015 IEEE Workshop on. IEEE, 2015, pp. 436–443.
  • [4] A. Narayanan and D. Wang, “Improving robustness of deep neural network acoustic models via speech separation and joint adaptive training,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 23, no. 1, pp. 92–101, 2015.
  • [5] K. Han, Y. Wang, and D. Wang, “Learning spectral mapping for speech dereverberation,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 4628–4632.
  • [6] K. Han, Y. Wang, D. Wang, W. S. Woods, I. Merks, and T. Zhang, “Learning spectral mapping for speech dereverberation and denoising,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 23, no. 6, pp. 982–992, 2015.
  • [7] K. Han, Y. He, D. Bagchi, E. Fosler-Lussier, and D. Wang, “Deep neural network based spectral feature mapping for robust speech recognition,” in Proc. Interspeech, 2015.
  • [8] D. Bagchi, M. I. Mandel, Z. Wang, Y. He, A. Plummer, and E. Fosler-Lussier, “Combining spectral feature mapping and multi-channel model-based source separation for noise-robust automatic speech recognition,” in Automatic Speech Recognition and Understanding (ASRU), 2015 IEEE Workshop on. IEEE, 2015, pp. 496–503.
  • [9] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” arXiv preprint arXiv:1611.07004, 2016.
  • [10] T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, “Improved techniques for training GAN,” in Advances in Neural Information Processing Systems, 2016, pp. 2234–2242.
  • [11] D. Michelsanti and Z.-H. Tan, “Conditional generative adversarial networks for speech enhancement and noise-robust speaker verification,” in Interspeech. ISCA, 2017.
  • [12] J. Ba and R. Caruana, “Do deep nets really need to be deep?,” in Advances in neural information processing systems, 2014, pp. 2654–2662.
  • [13] Y. Qian and P. C. Woodland, “Very deep convolutional neural networks for robust speech recognition,” in Spoken Language Technology Workshop (SLT), 2016 IEEE. IEEE, 2016, pp. 481–488.
  • [14] Z. Chen, S. Watanabe, H. Erdogan, and J. Hershey,

    “Integration of speech enhancement and recognition using long-short term memory recurrent neural network,”

    in Proc. Interspeech, 2015.
  • [15] F. Weninger, H. Erdogan, S. Watanabe, E. Vincent, J. Le Roux, J. R. Hershey, and B. Schuller, “Speech enhancement with lstm recurrent neural networks and its application to noise-robust ASR,” in International Conference on Latent Variable Analysis and Signal Separation. Springer, 2015, pp. 91–99.
  • [16] K. Markov and T. Matsui, “Robust speech recognition using generalized distillation framework.,” in Interspeech, 2016, pp. 2364–2368.
  • [17] S. Watanabe, T. Hori, J. Le Roux, and J. R. Hershey, “Student-teacher network learning with enhanced features,” in Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. IEEE, 2017, pp. 5275–5279.
  • [18] J. Li, M. L. Seltzer, X. Wang, R. Zhao, and Y. Gong, “Large-scale domain adaptation via teacher-student learning,” Proc. Interspeech 2017, pp. 2386–2390, 2017.
  • [19] E. Vincent, J. Barker, S. Watanabe, J. Le Roux, F. Nesta, and M. Matassoni, “The second ‘CHiME’ speech separation and recognition challenge: Datasets, tasks and baselines,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013, pp. 126–130.
  • [20] D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz, et al., “The Kaldi speech recognition toolkit,” in IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. IEEE Signal Processing Society, 2011, number EPFL-CONF-192584.
  • [21] O. S. Center, “Ohio supercomputer center,” http://osc.edu/ark:/19495/f5s1ph73, 1987.