Feature Learning in Deep Neural Networks - A Study on Speech Recognition
    Tasks
Authors: Dong Yu, Mike Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide
ICLR 2013

Abstract: Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper we argue that the difficulty in speech recognition is primarily caused by the high variability in speech signals. DNNs, which can be considered a joint model of a nonlinear feature transform and a log-linear classifier, achieve improved recognition accuracy by extracting discriminative internal representations that are less sensitive to small perturbations in the input features. However, if test samples are very dissimilar to training samples, DNNs perform poorly. We demonstrate these properties empirically using a series of recognition experiments on mixed narrowband and wideband speech and speech distorted by environmental noise.

Feature Learning in Deep Neural Networks – Studies
on Speech Recognition Tasks
Dong Yu, Michael L. Seltzer, Jinyu Li1, Jui-Ting Huang1, Frank Seide2
Microsoft Research, Redmond, W A 98052
1Microsoft Corporation, Redmond, W A 98052
2Microsoft Research Asia, Beijing, P.R.C.
{dongyu,mseltzer,jinyli,jthuang,fseide}@microsoft.com
Abstract
Recent studies have shown that deep neural networks (DNNs) perform signiﬁ-
cantly better than shallow networks and Gaussian mixture models (GMMs) on
large vocabulary speech recognition tasks. In this paper, we argue that the im-
proved accuracy achieved by the DNNs is the result of their ability to extract dis-
criminative internal representations that are robust to the many sources of variabil-
ity in speech signals. We show that these representations become increasingly in-
sensitive to small perturbations in the input with increasing network depth, which
leads to better speech recognition performance with deeper networks. We also
show that DNNs cannot extrapolate to test samples that are substantially differ-
ent from the training examples. If the training data are sufﬁciently representative,
however, internal features learned by the DNN are relatively stable with respect
to speaker differences, bandwidth differences, and environment distortion. This
enables DNN-based recognizers to perform as well or better than state-of-the-art
systems based on GMMs or shallow networks without the need for explicit model
adaptation or feature normalization.
1 Introduction
Automatic speech recognition (ASR) has been an active research area for more than ﬁve decades.
However, the performance of ASR systems is still far from satisfactory and the gap between ASR and
human speech recognition is still large on most tasks. One of the primary reasons speech recognition
is challenging is the high variability in speech signals. For example, speakers may have different
accents, dialects, or pronunciations, and speak in different styles, at different rates, and in different
emotional states. The presence of environmental noise, reverberation, different microphones and
recording devices results in additional variability. To complicate matters, the sources of variability
are often nonstationary and interact with the speech signal in a nonlinear way. As a result, it is
virtually impossible to avoid some degree of mismatch between the training and testing conditions.
Conventional speech recognizers use a hidden Markov model (HMM) in which each acoustic state
is modeled by a Gaussian mixture model (GMM). The model parameters can be discriminatively
trained using an objective function such as maximum mutual information (MMI) [1] or minimum
phone error rate (MPE) [2]. Such systems are known to be susceptible to performance degrada-
tion when even mild mismatch between training and testing conditions is encountered. To combat
this, a variety of techniques has been developed. For example, mismatch due to speaker differ-
ences can be reduced by V ocal Tract Length Normalization (VTLN) [3], which nonlinearly warps
the input feature vectors to better match the acoustic model, or Maximum Likelihood Linear Re-
gression (MLLR) [4], which adapt the GMM parameters to be more representative of the test data.
Other techniques such as Vector Taylor Series (VTS) adaptation are designed to address the mis-
match caused by environmental noise and channel distortion [5]. While these methods have been
1
arXiv:1301.3605v3  [cs.LG]  8 Mar 2013
successful to some degree, they add complexity and latency to the decoding process. Most require
multiple iterations of decoding and some only perform well with ample adaptation data, making
them unsuitable for systems that process short utterances, such as voice search.
Recently, an alternative acoustic model based on deep neural networks (DNNs) has been proposed.
In this model, a collection of Gaussian mixture models is replaced by a single context-dependent
deep neural network (CD-DNN). A number of research groups have obtained strong results on a
variety of large scale speech tasks using this approach [6–13]. Because the temporal structure of the
HMM is maintained, we refer to these models as CD-DNN-HMM acoustic models.
In this paper, we analyze the performance of DNNs for speech recognition and in particular, exam-
ine their ability to learn representations that are robust to variability in the acoustic signal. To do
so, we interpret the DNN as a joint model combining a nonlinear feature transformation and a log-
linear classiﬁer. Using this view, we show that the many layers of nonlinear transforms in a DNN
convert the raw features into a highly invariant and discriminative representation which can then be
effectively classiﬁed using a log-linear model. These internal representations become increasingly
insensitive to small perturbations in the input with increasing network depth. In addition, the classi-
ﬁcation accuracy improves with deeper networks, although the gain per layer diminishes. However,
we also ﬁnd that DNNs are unable to extrapolate to test samples that are substantially different from
the training samples. A series of experiments demonstrates that if the training data are sufﬁciently
representative, the DNN learns internal features that are relatively invariant to sources of variability
common in speech recognition such as speaker differences and environmental distortions. This en-
ables DNN-based speech recognizers to perform as well or better than state-of-the-art GMM-based
systems without the need for explicit model adaptation or feature normalization algorithms.
The rest of the paper is organized as follows. In Section 2 we brieﬂy describe DNNs and illustrate
the feature learning interpretation of DNNs. In Section 3 we show that DNNs can learn invariant
and discriminative features and demonstrate empirically that higher layer features are less sensitive
to perturbations of the input. In Section 4 we point out that the feature generalization ability is
effective only when test samples are small perturbations of training samples. Otherwise, DNNs
perform poorly as indicated in our mixed-bandwidth experiments. We apply this analysis to speaker
adaptation in Section 5 and ﬁnd that deep networks learn speaker-invariant representations, and to
the Aurora 4 noise robustness task in Section 6 where we show that a DNN can achieve performance
equivalent to the current state of the art without requiring explicit adaptation to the environment. We
conclude the paper in Section 7.
2 Deep Neural Networks
A deep neural network (DNN) is conventional multi-layer perceptron (MLP) with many hidden
layers (thus deep). If the input and output of the DNN are denoted as x and y, respectively, a
DNN can be interpreted as a directed graphical model that approximates the posterior probability
py|x(y= s|x) of a class sgiven an observation vector x, as a stack of (L+ 1)layers of log-linear
models. The ﬁrst Llayers model the posterior probabilities of hidden binary vectors hℓ given input
vectors vℓ. If hℓ consists of Nℓ hidden units, each denoted as hℓ
j, the posterior probability can be
expressed as
pℓ(hℓ|vℓ) =
Nℓ
∏
j=1
ezℓ
j(vℓ)·hℓ
j
ezℓ
j(vℓ)·1 + ezℓ
j(vℓ)·0 , 0 ≤ℓ<L
where zℓ(vℓ) = (Wℓ)T vℓ + aℓ, and Wℓ and aℓ represent the weight matrix and bias vector in the
ℓ-th layer, respectively. Each observation is propagated forward through the network, starting with
the lowest layer(v0 = x) . The output variables of each layer become the input variables of the next,
i.e. vℓ+1 = hℓ. In the ﬁnal layer, the class posterior probabilities are computed as a multinomial
distribution
py|x(y= s|x) = pL(y= s|vL) = ezL
s (vL)
∑
s′ ezL
s′ (vL) = softmaxs
(
vL)
. (1)
Note that the equality between py|x(y = s|x) and pL(y = s|vL) is valid by making a mean-ﬁeld
approximation [14] at each hidden layer.
2
In the DNN, the estimation of the posterior probability py|x(y= s|x) can also be considered a two-
step deterministic process. In the ﬁrst step, the observation vectorxis transformed to another feature
vector vL through L layers of non-linear transforms.In the second step, the posterior probability
py|x(y = s|x) is estimated using the log-linear model (1) given the transformed feature vector vL.
If we consider the ﬁrst L layers ﬁxed, learning the parameters in the softmax layer is equivalent
to training a conditional maximum-entropy (MaxEnt) model on features vL. In the conventional
MaxEnt model, features are manually designed [15]. In DNNs, however, the feature representations
are jointly learned with the MaxEnt model from the data. This not only eliminates the tedious and
potentially erroneous process of manual feature extraction but also has the potential to automatically
extract invariant and discriminative features, which are difﬁcult to construct manually.
In all the following discussions, we use DNNs in the framework of the CD-DNN-HMM [6–10]
and use speech recognition as our classiﬁcation task. The detailed training procedure and decoding
technique for CD-DNN-HMMs can be found in [6–8].
3 Invariant and discriminative features
3.1 Deeper is better
Using DNNs instead of shallow MLPs is a key component to the success of CD-DNN-HMMs. Ta-
ble 1, which is extracted from [8], summarizes the word error rates (WER) on the Switchboard
(SWB) [16] Hub5’00-SWB test set. Switchboard is a corpus of conversational telephone speech.
The system was trained using the 309-hour training set with labels generated by Viterbi align-
ment from a maximum likelihood (ML) trained GMM-HMM system. The labels correspond to
tied-parameter context-dependent acoustic states called senones. Our baseline WER with the cor-
responding discriminatively trained traditional GMM-HMM system is 23.6%, while the best CD-
DNN-HMM achives 17.0%—a 28% relative error reduction (it is possible to further improve the
DNN to a one-third reduction by realignment [8]).
We can observe that deeper networks outperform shallow ones. The WER decreases as the number
of hidden layers increases, using a ﬁxed layer size of 2048 hidden units. In other words, deeper mod-
els have stronger discriminative ability than shallow models. This is also reﬂected in the improve-
ment of the training criterion (not shown). More interestingly, if architectures with an equivalent
number of parameters are compared, the deep models consistently outperform the shallow models
when the deep model is sufﬁciently wide at each layer. This is reﬂected in the right column of the
table, which shows the performance for shallow networks with the same number of parameters as
the deep networks in the left column. Even if we further increase the size of an MLP with a single
hidden layer to about 16000 hidden units we can only achieve a WER of 22.1%, which is signiﬁ-
cantly worse than the 17.1% WER that is obtained using a 7×2k DNN under the same conditions.
Note that as the number of hidden layers further increases, only limited additional gains are obtained
and performance saturates after 9 hidden layers. The 9x2k DNN performs equally well as a 5x3k
DNN which has more parameters. In practice, a tradeoff needs to be made between the width of
each layer, the additional reduction in WER and the increased cost of training and decoding as the
number of hidden layers is increased.
3.2 DNNs learn more invariant features
We have noticed that the biggest beneﬁt of using DNNs over shallow models is that DNNs learn more
invariant and discriminative features. This is because many layers of simple nonlinear processing
can generate a complicated nonlinear transform. To show that this nonlinear transform is robust to
small variations in the input features, let’s assume the output of layerl−1, or equivalently the input
to the layer lis changed from vℓ to vℓ + δℓ, where δℓ is a small change. This change will cause the
output of layer l, or equivalently the input to the layer ℓ+ 1to change by
δℓ+1 = σ(zℓ(vℓ + δℓ)) −σ(zℓ(vℓ)) ≈diag
(
σ′(zℓ(vℓ))
)
(wℓ)T δℓ.
3
Table 1: Effect of CD-DNN-HMM network depth on WER (%) on Hub5’00-SWB using the 309-
hour Switchboard training set. DBN pretraining is applied.
L×N WER 1 ×N WER
1 ×2k 24.2 – –
2 ×2k 20.4 – –
3 ×2k 18.4 – –
4 ×2k 17.8 – –
5 ×2k 17.2 1 ×3772 22.5
7 ×2k 17.1 1 ×4634 22.6
9 ×2k 17.0 – –
5 ×3k 17.0 – –
– – 1 ×16k 22.1
Figure 1: Percentage of saturated activations at each layer
The norm of the change δℓ+1 is
∥δℓ+1∥≈∥ diag(σ′(zℓ(vℓ)))((wℓ)T δℓ)∥
≤∥diag(σ′(zℓ(vℓ)))(wℓ)T ∥∥δℓ∥
= ∥diag(vℓ+1 ◦(1 −vℓ+1))(wℓ)T ∥∥δℓ∥ (2)
where ◦refers to an element-wise product.
Note that the magnitude of the majority of the weights is typically very small if the size of the hidden
layer is large. For example, in a6×2k DNN trained using 30 hours of SWB data, 98% of the weights
in all layers except the input layer have magnitudes less than 0.5.
While each element in vℓ+1 ◦(1 −vℓ+1) is less than or equal to 0.25, the actual value is typically
much smaller. This means that a large percentage of hidden neurons will not be active, as shown in
Figure 1. As a result, the average norm ∥diag(vℓ+1 ◦(1 −vℓ+1))(wℓ)T ∥2 in (2) across a 6-hr SWB
development set is smaller than one in all layers, as indicated in Figure 2. Since all hidden layer
values are bounded in the same range of (0,1), this indicates that when there is a small perturbation
on the input, the perturbation shrinks at each higher hidden layer. In other words, features generated
by higher hidden layers are more invariant to variations than those represented by lower layers.
Note that the maximum norm over the same development set is larger than one, as seen in Figure
2. This is necessary since the differences need to be enlarged around the class boundaries to have
discrimination ability.
4 Learning by seeing
In Section 3, we showed empirically that small perturbations in the input will be gradually shrunk
as we move to the internal representation in the higher layers. In this section, we point out that the
4
Figure 2: Average and maximum ∥diag(vℓ+1 ◦(1 −vℓ+1))(wℓ)T ∥2 across layers on a 6×2k DNN
Figure 3: Illustration of mixed-bandwidth speech recognition using a DNN
above result is only applicable to small perturbations around the training samples. When the test
samples deviate signiﬁcantly from the training samples, DNNs cannot accurately classify them. In
other words, DNNs must see examples of representative variations in the data during training in
order to generalize to similar variations in the test data.
We demonstrate this point using a mixed-bandwidth ASR study. Typical speech recognizers are
trained on either narrowband speech signals, recorded at 8 kHz, or wideband speech signals,
recorded at 16 kHz. It would be advantageous if a single system could recognize both narrow-
band and wideband speech, i.e. mixed-bandwidth ASR. One such system was recently proposed
using a CD-DNN-HMM [17]. In that work, the following DNN architecture was used for all experi-
ments. The input features were 29 mel-scale log ﬁlter-bank outputs together with dynamic features.
An 11-frame context window was used generating an input layer with 29 ·3 ·11 = 957nodes. The
DNN has 7 hidden layers, each with 2048 nodes. The output layer has 1803 nodes, corresponding
to the number of senones determined by the GMM system.
The 29-dimensional ﬁlter bank has two parts: the ﬁrst 22 ﬁlters span 0–4 kHz and the last 7 ﬁlters
span 4–8 kHz, with the center frequency of the ﬁrst ﬁlter in the higher ﬁlter bank at 4 kHz. When the
speech is wideband, all 29 ﬁlters have observed values. However, when the speech is narrowband,
the high-frequency information was not captured so the ﬁnal 7 ﬁlters are set to 0. Figure 3 illustrates
the architecture of the mixed-bandwidth ASR system.
Experiments were conducted on a mobile voice search (VS) corpus. This task consists of internet
search queries made by voice on a smartphone.There are two training sets, VS-1 and VS-2, con-
sisting of 72 and 197 hours of wideband audio data, respectively. These sets were collected during
5
Table 2: WER (%) on wideband (16k) and narrowband (8k) test sets with and without narrowband
training data.
training data 16 kHz VS-T 8 kHz VS-T
16 kHz VS-1 + 16 kHz VS-2 27.5 53.5
16 kHz VS-1 + 8 kHz VS-2 28.3 29.3
different times of year. The test set, called VS-T, has 26757 words in 9562 utterances. The narrow
band training and test data were obtained by downsampling the wideband data.
Table 2 summarizes the WER on the wideband and narrowband test sets when the DNN is trained
with and without narrowband speech. From this table, it is clear that if all training data are wideband,
the DNN performs well on the wideband test set (27.5% WER) but very poorly on the narrowband
test set (53.5% WER). However, if we convert VS-2 to narrowband speech and train the same DNN
using mixed-bandwidth data (second row), the DNN performs very well on both wideband and
narrowband speech.
To understand the difference between these two scenarios, we take the output vectors at each layer
for the wideband and narrowband input feature pairs, hℓ(xwb) and hℓ(xnb), and measure their Eu-
clidean distance. For the top layer, whose output is the senone posterior probability, we calculate
the KL-divergence in nats between py|x(sj|xwb) and py|x(sj|xnb). Table 3 shows the statistics of dl
and dy over 40,000 frames randomly sampled from the test set for the DNN trained using wideband
speech only and the DNN trained using mixed-bandwidth speech.
Table 3: Euclidean distance for the output vectors at each hidden layer (L1-L7) and the KL di-
vergence (nats) for the posteriors at the top layer between the narrowband (8 kHz) and wideband
(16 kHz) input features, measured using the wideband DNN or the mixed-bandwidth DNN.
wideband DNN mixed-band DNN
layer dist mean variance mean variance
L1
Eucl
13.28 3.90 7.32 3.62
L2 10.38 2.47 5.39 1.28
L3 8.04 1.77 4.49 1.27
L4 8.53 2.33 4.74 1.85
L5 9.01 2.96 5.39 2.30
L6 8.46 2.60 4.75 1.57
L7 5.27 1.85 3.12 0.93
Top KL 2.03 – 0.22 –
From Table 3 we can observe that in both DNNs, the distance between hidden layer vectors generated
from the wideband and narrowband input feature pair is signiﬁcantly reduced at the layers close
to the output layer compared to that in the ﬁrst hidden layer. Perhaps what is more interesting
is that the average distances and variances in the data-mixed DNN are consistently smaller than
those in the DNN trained on wideband speech only. This indicates that by using mixed-bandwidth
training data, the DNN learns to consider the differences in the wideband and narrowband input
features as irrelevant variations. These variations are suppressed after many layers of nonlinear
transformation. The ﬁnal representation is thus more invariant to this variation and yet still has the
ability to distinguish between different class labels. This behavior is even more obvious at the output
layer since the KL-divergence between the paired outputs is only 0.22 in the mixed-bandwidth DNN,
much smaller than the 2.03 observed in the wideband DNN.
5 Robustness to speaker variation
A major source of variability is variation across speakers. Techniques for adapting a GMM-HMM to
a speaker have been investigated for decades. Two important techniques are VTLN [3], and feature-
space MLLR (fMLLR) [4]. Both VTLN and fMLLR operate on the features directly, making their
application in the DNN context straightforward.
6
Table 4: Comparison of feature-transform based speaker-adaptation techniques for GMM-HMMs,
a shallow, and a deep NN. Word-error rates in % for Hub5’00-SWB (relative change in parentheses).
GMM-HMM CD-MLP-HMM CD-DNN-HMM
adaptation technique 40 mix 1×2k 7×2k
speaker independent 23.6 24.2 17.1
+ VTLN 21.5 (-9%) 22.5 (-7%) 16.8 (-2%)
+ {fMLLR/fDLR}×4 20.4 (-5%) 21.5 (-4%) 16.4 (-2%)
VTLN warps the frequency axis of the ﬁlterbank analysis to account for the fact that the precise lo-
cations of vocal-tract resonances vary roughly monotonically with the physical size of the speaker.
This is done in both training and testing. On the other hand, fMLLR applies an afﬁne transform
to the feature frames such that an adaptation data set better matches the model. In most cases, in-
cluding this work, ‘self-adaptation’ is used: generate labels using unsupervised transcription, then
re-recognize with the adapted model. This process is iterated four times. For GMM-HMMs, fM-
LLR transforms are estimated to maximize the likelihood of the adaptation data given the model.
For DNNs, we instead maximize cross entropy (with back propagation), which is a discriminative
criterion, so we prefer to call this transform feature-space Discriminative Linear Regression (fDLR).
Note that the transform is applied to individual frames, prior to concatenation.
Typically, applying VTLN and fMLLR jointly to a GMM-HMM system will reduce errors by 10–
15%. Initially, similar gains were expected for DNNs as well. However, these gains were not
realized, as shown in Table 4 [9]. The table compares VTLN and fMLLR/fDLR for GMM-HMMs,
a context-dependent ANN-HMM with a single hidden layer, and a deep network with 7 hidden
layers, on the same Switchboard task described in Section 3.1. For this task, test data are very
consistent with the training, and thus, only a small amount of adaptation to other factors such as
recording conditions or environmental factors occurs. We use the same conﬁguration as in Table 1
which is speaker independent using single-pass decoding.
For the GMM-HMM, VTLN achieves a strong relative gain of 9%. VTLN is also effective with the
shallow neural-network system, gaining a slightly smaller 7%. However, the improvement of VTLN
on the deep network with 7 hidden layers is a much smaller 2% gain. Combining VTLN with
fDLR further reduces WER by 5% and 4% relative, for the GMM-HMM and the shallow network,
respectively. The reduction for the DNN is only 2%. We also tried transplanting VTLN and fMLLR
transforms estimated on the GMM system into the DNN, and achieved very similar results [9].
The VTLN and fDLR implementations of the shallow and deep networks are identical. Thus, we
conclude that to a signiﬁcant degree, the deep neural network is able to learn internal representations
that are invariant with respect to the sources of variability that VTLN and fDLR address.
6 Robustness to environmental distortions
In many speech recognition tasks, there are often cases where the despite the presence of variability
in the training data, signiﬁcant mismatch between training and test data persists. Environmental
factors are common sources of such mismatch, e.g. ambient noise, reverberation, microphone type
and capture device. The analysis in the previous sections suggests that DNNs have the ability to
generate internal representations that are robust with respect to variability seen in the training data.
In this section, we evaluate the extent to which this invariance can be obtained with respect to
distortions caused by the environment.
We performed a series of experiments on the Aurora 4 corpus [18], a 5000-word vocabulary task
based on the Wall Street Journal (WSJ0) corpus. The experiments were performed with the 16 kHz
multi-condition training set consisting of 7137 utterances from 83 speakers. One half of the ut-
terances was recorded by a high-quality close-talking microphone and the other half was recorded
using one of 18 different secondary microphones. Both halves include a combination of clean speech
and speech corrupted by one of six different types of noise (street trafﬁc, train station, car, babble,
restaurant, airport) at a range of signal-to-noise ratios (SNR) between 10-20 dB.
7
The evaluation set consists of 330 utterances from 8 speakers. This test set was recorded by the pri-
mary microphone and a number of secondary microphones. These two sets are then each corrupted
by the same six noises used in the training set at SNRs between 5-15 dB, creating a total of 14 test
sets. These 14 test sets can then be grouped into 4 subsets, based on the type of distortion: none
(clean speech), additive noise only, channel distortion only, and noise + channel. Notice that the
types of noise are common across training and test sets but the SNRs of the data are not.
The DNN was trained using 24-dimensional log mel ﬁlterbank features with utterance-level mean
normalization. The ﬁrst- and second-order derivative features were appended to the static feature
vectors. The input layer was formed from a context window of 11 frames creating an input layer of
792 input units. The DNN had 7 hidden layers with 2048 hidden units in each layer and the ﬁnal
softmax output layer had 3206 units, corresponding to the senones of the baseline HMM system.
The network was initialized using layer-by-layer generative pre-training and then discriminatively
trained using back propagation.
In Table 5, the performance obtained by the DNN acoustic model is compared to several other
systems. The ﬁrst system is a baseline GMM-HMM system, while the remaining systems are repre-
sentative of the state of the art in acoustic modeling and noise and speaker adaptation. All used the
same training set. To the authors’ knowledge, these are the best published results on this task.
The second system combines Minimum Phone Error (MPE) discriminative training [2] and noise
adaptive training (NAT) [19] using VTS adaptation to compensate for noise and channel mismatch
[20]. The third system uses a hybrid generative/discriminative classiﬁer [21] as follows . First, an
adaptively trained HMM with VTS adaptation is used to generate features based on state likelihoods
and their derivatives. Then, these features are input to a discriminative log-linear model to obtain the
ﬁnal hypothesis. The fourth system uses an HMM trained with NAT and combines VTS adaptation
for environment compensation and MLLR for speaker adaptation [22]. Finally, the last row of the
table shows the performance of the DNN system.
Table 5: A comparison of several systems in the literature to a DNN system on the Aurora 4 task.
Systems
distortion
A VGnone noise channel noise +
(clean) channel
GMM baseline 14.3 17.9 20.2 31.3 23.6
MPE-NAT + VTS [20] 7.2 12.8 11.5 19.7 15.3
NAT + Derivative Kernels [21] 7.4 12.6 10.7 19.0 14.8
NAT + Joint MLLR/VTS [22] 5.6 11.0 8.8 17.8 13.4
DNN (7×2048) 5.6 8.8 8.9 20.0 13.4
It is noteworthy that to obtain good performance, the GMM-based systems required complicated
adaptive training procedures [19, 23] and multiple iterations of recognition in order to perform ex-
plicit environment and/or speaker adaptation. One of these systems required two classiﬁers. In
contrast, the DNN system required only standard training and a single forward pass for classiﬁ-
cation. Yet, it outperforms the two systems that perform environment adaptation and matches the
performance of a system that adapts to both the environment and speaker.
Finally, we recall the results in Section 4, in which the DNN trained only on wideband data could
not accurately classify narrowband speech. Similarly, a DNN trained only on clean speech has no
ability to learn internal features that are robust to environmental noise. When the DNN for Aurora
4 is trained using only clean speech examples, the performance on the noise- and channel-distorted
speech degrades substantially, resulting in an average WER of 30.6%. This further conﬁrms our
earlier observation that DNNs are robust to modest variations between training and test data but
perform poorly if the mismatch is more severe.
7 Conclusion
In this paper we demonstrated through speech recognition experiments that DNNs can extract more
invariant and discriminative features at the higher layers. In other words, the features learned by
8
DNNs are less sensitive to small perturbations in the input features. This property enables DNNs to
generalize better than shallow networks and enables CD-DNN-HMMs to perform speech recogni-
tion in a manner that is more robust to mismatches in speaker, environment, or bandwidth. On the
other hand, DNNs cannot learn something from nothing. They require seeing representative samples
to perform well. By using a multi-style training strategy and letting DNNs to generalize to similar
patterns, we equaled the best result ever reported on the Aurora 4 noise robustness benchmark task
without the need for multiple recognition passes and model adaptation.
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Peer review:
[Review]:
We’d like to thank the reviewers for their comments. 

We have uploaded a revised version of the paper which we believe addresses reviewers’ concerns as well as the grammatical issues and typos. 
 
We have revised the abstract and introduction to better establish the purpose of the paper. Our goal is to demonstrate that deep neural networks can learn internal representations that are robust to variability in the input, and that this robustness is maintained when large amounts of training data are used. Much work in DNNs has been on smaller data sets and historically, in speech recognition, large improvements observed on small systems usually do not translate when applied to large-scale state-of-the-art systems. 
 
In addition, the paper contrasts DNN-based systems and their “built in” invariance to a wide variety of variability, to GMM-based systems, where algorithms have been designed to combat unwanted variability in a source-specific manner, i.e. they are designed to address a particular mismatch, such as the speaker or the environment. 
 
We also believe there is also a practical implication of these results: algorithms for addressing this acoustic mismatch in speaker, environment, or other factors, which are standard and essential for GMM-based recognizers, become far less critical and potentially unnecessary for DNN-based recognizers. We think this is important for both setting future research directions and deploying large-scale systems. 
 
Finally, while some of the results have been published previously, we believe the inherent robustness of  DNNs to such diverse sources of variability is quite interesting, and is a point that might allude readers unless these results are combined and presented together. We also want to point out that the analysis of sensitivity to the input perturbation and all of the results in Section 6 on environmental robustness are new and previously unpublished. 

We hope by putting together all these analyses and results in one paper we can provide some insights on the strengths and weaknesses of using a DNN for speech recognition when trained with real world data.