R EAL - TIME N EURAL V OICE C AMOUFLAGE
Anonymous authors
Paper under double-blind review
A BSTRACT
Automatic speech recognition systems have created exciting possibilities for applications, however they also enable opportunities for systematic eavesdropping.
We propose a method to camouﬂage a person’s voice over-the-air from these systems without inconveniencing the conversation between people in the room.
Standard adversarial attacks are not effective in real-time streaming situations because the characteristics of the signal will have changed by the time the attack is executed.
We introduce predictive attacks, which achieve real-time performance by forecasting the attack that will be the most effective in the future.
Under real-time constraints, our method jams the established speech recognition system DeepSpeech 4.17x more than baselines as measured through word error rate, and 7.27x more as measured through character error rate.
We furthermore demonstrate our approach is practically effective in realistic environments over physical distances.
I NTRODUCTION
Nowadays, automatic speech recognition models are embedded in nearly all smart devices.
Although these models have many exciting applications, the concern for the potential of these devices to eavesdrop is signiﬁcant.
It is becoming increasingly important to develop methods that give users the autonomy to safeguard their speech from voice processing software.
Fortunately, over the last decade, there has been work demonstrating that neural networks models are easily fooled.
For example, they remain vulnerable to small additive perturbations (Carlini &
Wagner, 2018), ambient noise (Xu et al., 2020), and unusual examples (Nguyen et al., 2015).
Predominant methods such as gradient-based methods and their variants have remained the standard approach to generating challenging examples for deep neural networks (Madry et al., 2019).
However, to achieve this, these methods require the full input upfront, and thus users can not practically use them as they continuously speak.
Therefore, the community has increasingly been focusing on researching general, robust methods of breaking neural networks that can be used in real-time.
We deﬁne robust to mean an obstruction that can not be easily removed, real-time to mean an obstruction that is generated continuously as speech is spoken, and general to mean applicable to the majority of vocabulary in a language.
Existing prior work has successfully tackled at least one of these three requirements, but none all three.
While some work is real-time (Chen et al., 2020; Sch¨onherr et al., 2018), these disruptions can be ﬁltered out as
Human Speech
Predictive Attack +
WE WON’T TALK ABOUT HER ANY MORE IY FEED RATHERN MOT WITH FLIGHTEND WE
AN DED WTIH ASR they are constrained to speciﬁc frequency ranges.
Universal attacks (Lu et al., 2021) can be similarly subtracted.
Gong et al.
(2019) achieved both real-time and robust obstructions, but are limited to a predeﬁned set of ten words.
Streaming audio is a particularly demanding domain to disrupt because the calculation needs to be performed in real-time.
By the time a sound is computed, time will have passed and the streaming signal will have changed, making standard generative methods obsolete.
The sampling rate of audio is at least 16 kHz, meaning the corruption for a given input must be estimated and played over a speaker within milliseconds, which is currently infeasible.
Additionally, when attacks are played over-the-air, the attack needs to be loud enough to disrupt any rogue microphone that could be far away.
The attack sound needs to carry the same distance as the voice.
We introduce predictive attacks, which are able to disrupt any word that automatic speech recognition models are trained to transcribe.
Our approach achieves real-time performance by forecasting an attack on the future of the signal, conditioned on two seconds of input speech.
Our attack is optimized to have a volume similar to normal background noise, allowing people in a room to converse naturally and without monitoring from an automatic speech recognition system.
Forecasting with deep neural networks has already been successfully used in other domains to achieve real-time performance, for instance in packet loss concealment (Pascual et al., 2021).
In this paper, we demonstrate how and why this approach lends itself particularly well to developing general, robust and real-time attacks for automatic speech recognition models.
Our experiments show that predictive attacks are able to largely disrupt the established DeepSpeech (Amodei et al., 2016) recognition system which was trained on the LibriSpeech dataset (Panayotov et al., 2015).
On the standard, large-scale dataset LibriSpeech, our approach causes at least a three fold increase in word error rate over baselines, and at least a six fold increase in character error rate.
Our method is practical and straightforward to implement on commodity hardware.
We additionally demonstrate the method works inside real-world rooms with natural ambient noise and complex scene geometries.
We call our method Neural Voice Camouﬂage.
R ELATED W ORK
Breaking Neural Networks: Szegedy et al.
ﬁrst discovered adversarial attacks in computer vision.
Since then, a large number of methods to break neural networks have been introduced (Madry et al., 2019; Kurakin et al., 2017; Carlini & Wagner, 2017; Croce & Hein, 2020;
MoosaviDezfooli et al., 2016; Goodfellow et al., 2014) , where noise optimized by gradient descent fool the state-of-the-art models.
Audio adversarial attacks (Carlini & Wagner, 2018; Qin et al., 2019; Yakura & Sakuma, 2019; Sch¨onherr et al., 2018) have also been constructed.
Gradient based iterative adversarial attacks, while effective, are computationally intensive, and need to see the whole example ﬁrst before launching the attack.
Faster adversarial attacks uses generators to generate attacks (Xiao et al., 2019).
However, the attacks are still ofﬂine.
To make the adversarial attack reliable for live speech, the attacker needs to anticipate the future in an online manner.
Online Attacks: Real-time attacks are an emerging area of research in machine learning and there have been several initial works.
For example, Gong et al.
(2019) develop a reinforcement learning based approach to balance the trade-off between number of samples seen and attack deployment duration.
They also optimize a volume trade-off to achieve over-the-air performance.
While they learn to disrupt spoken keyword detection (a predeﬁned set of ten words), our approach is able to obfuscate entire sentences.
Further, attacks for streaming data with bayesian approaches have been proposed (Braverman et al., 2021; Seraphim & Poovammal, 2021).
However, they are unable to tackle high-dimensional data such as audio.
Another direction prior work has taken to create online attacks is to constantly be attacking a certain word (Li et al., 2019).
Although this works in realtime, it only targets the wake word, and not full sentences.
There also have been a few methods that jam microphones by emitting sound outside the range of human hearing.
For example, Chen et al.
(2020) developed an approach to emit ultrasonic attacks and Sch¨onherr et al.
(2018) also generate attacks outside the human hearing range.
However, by limiting the attack to speciﬁc frequencies, a defender can design a microphone that ﬁlters this set of frequencies out.
Robustness: Due to the importance of this problem, there has been extensive research into learning robust models (Madry et al., 2019; Carmon et al., 2019; Wang et al., 2020; Mao et al., 2019; 2020;
[Figure]
However, building defenses is challenging, and work has even shown that many basic defenses, such as adding randomness to the input, are not effective (Athalye et al., 2018).
Among all the defense strategies, adversarial training proposed by Madry et al.
(2019) is the standard defense that has been most widely used.
However, adversarial training has the drawback that it improves robustness accuracy at the cost of reducing the original accuracy (Tsipras et al., 2019), which is the reason that adversarial training is not used in most real-world applications.
In this paper, we show our approach is still effective against these established defenses.
Real-time Machine Learning: Interest in real-time artiﬁcal intelligence dates back to 1996, starting with anytime algorithms, which return a solution at any given point in time (Zilberstein, 1996).
More recently, there have been challenges to evaluate vision models in real-time (Kristan et al., 2017).
Generally, there has been a focus on speeding up forward passes to allow for faster inference, thereby approaching real-time (Howard et al., 2017).
In addition, there has been recent work in leveraging deep neural network predictions to achieve real-time performance, which has been applied to speech packet loss concealment (Pascual et al., 2021). This differs from the previous approaches of improving inference speed. Recently, the community has recently taken an interest in establishing robust metrics and evaluations for real-time inference (Li et al., 2020).
M ETHOD
We present our approach for creating real-time obstructions to automatic speech recognition (ASR) systems.
We ﬁrst motivate the background for real-time attacks, then introduce our approach that achieves online performance through predictive attack models.
3. S TREAMING S PEECH R ECOGNITION
Let x t be a streaming signal that represents the input speech up until time t .
The goal of ASR is to transcribe this signal into the corresponding text y t .
The ﬁeld often estimates this mapping through a neural network ˆ y t
= f ψ ( x t ) where the parameters ψ are optimized to minimize the empirical risk min ψ E ( x,y )
[ L (ˆ y t , y t )] .
For modeling sequences, the CTC loss function is a common choice for L (Graves et al., 2006).
In ofﬂine setups, we can corrupt the neural network f ψ with a standard adversarial attack.
These attacks work by ﬁnding a minimal additive perturbation vector α t that, when added to the input signal, produces a high loss: arg max α t L ( f ψ ( x t + α t ) , y t ) subject to a bound on the norm of the perturbation (cid:107) α t (cid:107) ∞ < (cid:15) .
Adversarial attacks, such as projected gradient descent (PGD) or fast gradient descent, have been widely effective on vision and speech datasets (Madry et al., 2019;
Goodfellow et al., 2014; Carlini & Wagner, 2018).
Defending against them both empirically and theoretically remains an active area of research today.
Standard adversarial attacks will optimize the perturbation vector α t conditioned on the current position of the stream x t .
However, by the time the solution α t is found for a stream, the attack will be obsolete because time will have passed and the condition will have almost certainly changed.
Audio is a particularly demanding domain because the high sampling rate (as high as 48 kHz) would require attacks to be computed nearly instantaneously (less than 20 microseconds).
Furthermore, applying the stale α t to the future x t + δ will not work because the attack vectors are optimized to corrupt the features of their input, which may vary over time. [Figure] d A tt acke d
A tt acke d
C l ea n C l ea n C l ea n 3. P REDICTIVE R EAL -T IME A TTACKS
We propose a class of predictive attacks, which enable real-time performance by forecasting the attack vector that will be effective in future time steps.
It will invariably take some time for the attack to be computed.
For attacks to operate in real-time environments, this means the attack needs to be optimized not for the observed signal, but for the unobserved signal in the future.
If our observation of the signal x t is captured at time t and our algorithm takes δ seconds to compute an attack and play it, then we need to attack the signal starting at x t + δ .
However, creating these attacks is challenging practically because real-world signals are stochastic and multi-modal.
Due to the high uncertainty, generating future speech x t + δ for the purpose of computing attacks is infeasible.
Rather than forecasting the signal, we will learn to forecast the attack vector, which encloses all possible variations of the next utterances conditioned on the current input.
This attack will learn to “hedge the bet” by ﬁnding a single, minimal pattern that robustly obstructs all upcoming possibilities.
Under the perturbation bound (cid:15) , we model predictive attacks as:
[Equation]
where g θ is a predictive model conditioned on the present input speech and paramaterized by θ .
To be consistent with our notation, which represents x t as the signal until time t , we include an additional offset r to represent the temporal duration of the attack.
To satisfy the constraint on the perturbation bound, we use the tanh activation function to squash the range to the interval [ − 1 , 1] before multiplying the result by a scalar (cid:15) .
This (cid:15) is equal to the product of a predetermined multiplier m and the maximum of the absolute value of the input speech waveform.
With predictive attacks, the algorithm for generating obstructions in real-time becomes straightforward.
After the microphone observes x t , the speakers need to play α t + δ + r exactly δ seconds later.
Since sound is additive modulo reverberation, this will cause a third-party microphone to receive the corrupted signal x t
+ δ + r + g θ ( x t ) .
We found modeling the room acoustics was unnecessary because signiﬁcant reverberation already breaks state-of-the-art ASR models.
We will use neural networks to instantiate the predictive model g .
To obtain real-time performance, our feed forward calculation needs to be less than the delay δ into the future.
On commodity hardware today, this calculation is on the order of 50 milliseconds.
3. L EARNING
We will learn the parameters θ of our predictive model from a large-scale labelled speech dataset, for a speciﬁc automatic speech recognition model f ψ .
We formulate this as the maximization problem:
[Equation]
s.t.
[Equation]
and (cid:107)
[Equation]
[Equation]
where ¯ y t is the result of the ASR model after our attack to x t .
The objective will drive the model to ﬁnd attacks that, in the future of the signal, will maximize the expected loss of the ASR model.
We optimize θ using stochastic gradient descent while keeping ψ ﬁxed.
Once training is performed ofﬂine, inference is efﬁcient, requiring just a single feed-forward computation.
3. I MPLEMENTATION D ETAILS
The input to our network g θ is the Short-Term Fourier Transform (STFT) of the last 2 seconds of the speech signal.
The network outputs a waveform of 0 .
5 seconds, sampled at 16 kHz.
To calculate the
STFT, we use a hamming window length of 320 samples, hop length of 160 samples, and FFT size of 320 , resulting in an input dimension of 2 × 161 × 204 .
We use a 13 layer convolutional network.
The appendix has full network details.
We also experimented with a network that ouputs an STFT instead of waveform.
However, regressing an STFT has no guarantee that there will be a corresponding waveform to it, which means we can not actually play it in practice.
In order to prevent this from happening, there has been work that adds an additional term to minimize the loss between the predicted STFT and the nearest valid
STFT (Maraﬁoti et al., 2019).
However, we found that predicting the waveform directly was both simpler and more effective.
Speech datasets generally do not have time stamps to their transcriptions.
In order to train our model, we need to compute the loss between the predicted speech and the ground-truth speech, meaning that in training, we need to attack the entire speech signal, not just a small segment.
We therefore need to schedule our forward and backward passes such that we have computed the attack for the entire segment before we calculate the gradients.
We optimized our predictive network g θ for 4 epochs with batch size 32 across 8 NVIDIA RTX 2080 Ti GPUs on the 100 -hour LibriSpeech dataset.
This computation took approximately 2 days.
The learning rate started at 1 . 5 · 10 − 4 and decreased using an exponential learning rate scheduler, with a learning anneal gamma value of 0 . 99 .
Our code was written in PyTorch (Paszke et al., 2019) and PyTorch-Lightning (Falcon et al., 2019).
E XPERIMENTS
The objective of our experiments is to analyze predictive attacks under the constraints of real time speech streams.
We ﬁrst introduce the experimental setup, baselines, and defense models.
We then present our experimental results with both quantitative and qualitative evidence.
DeepSpeech: We ﬁrst consider the DeepSpeech automatic speech recognition system (Hannun et al., 2014), which is a commonly used pretrained model. We build off of an open-sourced implementation and use pretrained model checkpoints. 1 Since we train and test on the model, this evaluates the white box behavior of our approach.
Wav2Vec2:
Additionally, we evaluate our approach in a black box setting by generating attacks for speech signals and then passing them through the Wav2Vec2 automatic speech recognition model(Baevski et al., 2020), without retraining our predictive model with Wav2Vec2.
We use the implementation from huggingface (Wolf et al., 2019).
LibriSpeech Dataset: We train on the LibriSpeech clean 100 hour dataset, validate on the LibriSpeech clean development set, and test on the LibriSpeech test set.
For our approach, we restrict the amplitude of our predicted attack to be 0.008 times the maximum of the absolute value of the amplitude of the speech signal.
We call this the relative amplitude throughout the paper.
Intuitively, our attack sounds similar to the sound of a quiet air-conditioner in the background.
We additionally evaluate on several baselines, including various levels of white noise as well as projected gradient descent.
For some of baselines, we experimented with making the amplitude louder, but never below the amplitude of our predicted attack.
In order to measure the time taken fairly, we measured the time necessary to create the attack vector for an input of two seconds averaged over 200 runs. [Table]
A TTACK M ETHODS AND M ETRICS
To evaluate our approach, we compare against several methods to obstruct the speech signal.
Uniform Noise: One straight-forward way to obstruct speech is to play white noise.
We use the same amplitude that our attack uses.
We also experimented with amplitudes that are an order of magnitude louder than our attack.
Ofﬂine Projected Gradient Descent (PGD): Projected gradient descent is the standard method for attacking speech samples (Madry et al., 2019).
It calculates the gradient of the attack using back-propagation, adds this gradient multiplied by a step size to the attack vector, and projects this sum back into the valid bound by clipping if the gradient exceeds the designated range.
For the
DeepSpeech model, we run 10 iterations of projected gradient descent on the input speech signal with step size equal to 20% of the bound.
For the denoiser, we ran gradient descent for 30 iterations.
Since projected gradient descent requires access to the entire signal and cannot be conducted online, we use this baseline to understand what the best attack could be if we had access to the future.
Online Projected Gradient Descent (PGD): Ofﬂine projected gradient descent does not work in real-time, since PGD requires the entire input signal in order to optimize the attack vector, and by the time the input signal is recorded, it has already passed.
To make an online version of PGD, we calculate the PGD from the window of the input stream in the same manner as described for the ofﬂine method, but apply it to the future time.
We note that this is unfair to our own approach, because PGD is at least two orders of magnitude slower than our approach.
Our Approach: We ﬁnally evaluate our approach, which requires just a single forward pass per half second of input speech.
Our attack takes 0 .
014 seconds for a single forward pass, meaning that we need to be forecasting at least that amount into the future.
We experimented with several options for how far into the future we forecast, using larger delays to allow time for recording speech and play back of attack (0.5s, 0.75s, 1.0s).
We found that 0.5s performed best empirically.
The most common way to evaluate speech recognition models is through word and character error rates.
We evaluate our attacks at their capability to increase errors.
Word Error Rate (WER) measures the proportion of words that were incorrectly predicted, deﬁned as ( S w + D w + I w ) /N
w , where
S w represents the number of word substitutions, D w the number of word deletions, I the number of word insertions, and N w is the number of words.
Character Error Rate (CER) measures the proportion of characters that were incorrectly predicted, which is important to analyze because our attack might just disrupt a single letter but the word is still intact.
It is deﬁned as ( S c + D c + I c ) /C
c , where S c represents the number of character substitutions, D c the number of character deletions, I the number of character insertions, and N c is the number of characters.
4. R OBUST M ODELS
We evaluate our approach with both standard automatic speech recognition models as well as their robust counterparts.
There are two main methods to make models robust: input preprocessing methods and adversarial training (Zelasko et al., 2021).
The former fortiﬁes the mode by attempting to clean the data of the attack, and the latter by strengthening the robustness of the model against attacks.
We implement both methods to evaluate our approach.
[Figure]
[Figure]
Language Model: We also added a language model during decoding to help defend against the attacks.
We use the same language model that comes standard with DeepSpeech.
The language model can act as a prior to both constrain transcriptions to natural words and using the sentence context to help correct word errors.
Audio Denoiser: The standard way to suppress noise is to denoise the input signal.
We use a state-of-the-art audio denoiser on the attacked inputs (Xu et al., 2020).
In order to make our attacks robust to this form of preprocessing, we retrain our predictive model g θ , this time passing ˆ y x t through the denoiser model h φ , before passing it the automatic speech recognition model: ¯ y t
= f ψ ( h φ ( x t + g θ ( x t − r − δ ))) .
Once again, we keep the automatic speech recognition model f ψ and the denoiser model h
φ ﬁxed, while updating our predictive model g θ .
Adversarial Training: In addition, we use adversarial training to create a robust speech recognition system.
We ﬁne-tune the automatic speech recognition model on the adversarial examples.
To maintain the performance on clean examples, we also add regular inputs in our training, following standard practice (Zhang et al., 2019).
We train the DeepSpeech model f ψ , and every batch contains half clean inputs, and half attacked inputs with 3 steps of projected gradient descent.
This is already more attack than the fast-adversarial training approaches (Wong et al., 2020), as they use 1 step, making this model very robust.
We call the robust model f (cid:48) ψ .
There is always a trade-off between robustness and clean accuracy (Tsipras et al., 2019).
We stop training once the WER on the attacked inputs dropped sufﬁciently, from 100 .
9% to 82 .
8% , and when the WER on the clean inputs increased from 11 .
3% to 18 .
As is standard practice with adversarial training, we retrained our predictive model g θ with the new robust f (cid:48) ψ , giving g (cid:48) θ .
4. Q UANTITATIVE A NALYSIS
Table 1 shows that our predictive attack is able to signiﬁcantly disrupt automatic speech recognition systems.
When we evaluate with the standard model (DeepSpeech column), the predictive attack is able to produce a WER that is over four times more effective than a standard online PGD attack.
The white noise is able to corrupt the signal, but it requires substantially more amplitude than our approach.
Even when the white noise amplitude is an order of magnitude larger than ours, our method is still more effective.
We see a similar rate with the CER, suggesting that its completely corrupting words.
The performance of the ofﬂine attack shows that observing the future is able to further improve the error rate, at the cost of not being able to be real-time.
We furthermore found that our attack does not signiﬁcantly impact the clarity of the speech for humans to understand it.
We asked human subjects to manually transcribe both attacked inputs and non-attacked inputs, and we found people only made 4% more errors when our attack is present.
Results with Language Model: Many ASR models use a language model during prediction, and we also evaluate our attack in that setting. For clean words, our results show that as expected the language model helps decrease the WER by 1 . 8% . Similarly, across the white noise baselines, the language model helps decrease the WER. However, our attack actually performed better when there is a language model present.
By visualizing the transcripts, we found that our attack causes the language model to separate non-existent words into two words because the language model acts as a prior to snap the predictions to real English words.
This increase in the word count causes the WER to increase.
We see similar trends with CER.
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{+1736.0b 0.3423728813559320 1770.0 38 p 0.36521739130434800 805.
+}
[Figure]
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[Figure]
Results with Defense: We next evaluate our model when the ASR system has a defense mechanism.
As expected, the defense mechanisms cause the WER for the attacked inputs to go down for all attack approaches.
However, our attack still outperforms the baselines by nearly double in both cases.
The denoiser is particularly effective at removing the white noise, which is expected as this is what it is trained to do.
Moreover, the state of the art methods for audio denoising, published just last year (Xu et al., 2020), are actually very effective at removing PGD attacks.
However, our approach still manages to fool the denoiser.
We also ran inference on the adversarially trained DeepSpeech without retraining our predictive model.
Training with the defense improves the WER by only 8 . 4% and the CER 7 . 8% .
This shows that our method is still strong even without updating our own predictive model when the automatic speech recognition model has been retrained to be more robust.
Results with Black-box: We also evaluated our attack when the ASR model is different from the one during training, which corresponds to a black-box attack.
We apply our attack to the Wav2Vec2, and the results show that our attack is still effective.
With the same attack multiplicant, we outperform the white noise baselines on WER signiﬁcantly ( 3 . 9% compared to 28 . 0% ).
Furthermore, the only baseline that outperforms our attack is the online PGD approach, but unlike ours, this baseline is a white-box attack and consequently had access to more information.
4. C HARACTERIZING THE A TTACK
In order to analyze how our model produces attacks, we performed several quantitative experiments.
Does the model attack speciﬁc instances?
Our attack model learns which frequencies to produce at each time in order to maximize the error rate of the ASR system.
To investigate whether the attacks were adaptive to the current input, we swapped attacks from different speakers.
Since input speech signals have varying lengths, the attacks will also have varying lengths.
For the attack that is shorter than the speech, we repeat the attack until the entire speech covered.
Conversely, if the attack is longer than the speech, then we cut it short.
Our results shows that by swapping attacks for instances, the WER and CER both drop.
In doing so, the WER drops from 81 .
5% to 35 .
0% and the CER drops from 53 . 6% to 19 .
This indicates that the model is predicting corruptions that are adaptive to the speciﬁc input.
How robust is the attack to temporal shifts?
In practical settings, the attack may not launch at exactly the right time due to various delays in software and hardware systems.
Therefore, we analyze how the delay δ inﬂuences the success of our attack.
The larger the delay δ , the further into the future our model needs to predict.
We train a model to predict δ = 0 into the future, and Figure 4 shows [Figure] [Table] [Figure]
[Table] [Figure] [Table] [Figure]
[Table]
[Figure]
that, when it is applied for a larger delay δ , the WER drops.
There are two factors that could explain this drop.
The ﬁrst is that as the delay increases, there is a shorter amount of time for which the speech is attacked.
The second factor is that as the delay increases, we are predicting further into the future, thus the future becomes more uncertain.
In order to disentangle these two factors, for each different delay, we linearly scale the error rate in proportion to the decrease of time for which the attack is active (shown in the dashed line).
The decreasing plot shows that the attack is sensitive to the timing even when we factor in the reduced time to deploy it.
This suggests the model is learning to predict key features about the upcoming speech.
However, the performance drop is not severe, showing our approach is relatively robust during inaccurate timing.
How does power impact attack performance?
In ofﬂine settings, normal adversarial attacks aim to reduce the volume as much as possible.
However, this is more challenging in over-the-air settings because there may be background ambient noise and the rogue recording device may be far away.
[-.0 48 u 0.2744082840236690 1352.0 27 l 0.2749266862170090 2728.0 102 m 0.28125 2880.0 68 k 0.2817098808689560 1427.0 63 ŋ 0.2961082910321490 591.0 28 f 0.30184331797235000 1736.0 47 b 0.3423728813559320 1770.0 38 p 0.36521739130434800 805.
-]
To investigate which level of amplitude a person should select, Figure 5 shows there is a linear relation between the power and the error rate until a critical point at about 0 .
In our real world experiments, we found the value of 0 .
008 to be effective in real room environments.
What makes a word easy or hard to attack?
For each word, Figure 6a plots the recognition accuracy both before and after the attack.
Since most of the points are below the diagonal line, this shows our attack is effective for most words.
However, some words drop in accuracy more than others.
Figure 6b compares this drop in accuracy to how often the word is spoken in the dataset.
The results show that the most common words (e.g. “the”, “our”, “they”) are the most difﬁcult to disrupt.
However, by deﬁnition, the common words carry low information content, thus making them less crucial to attack.
Figure 6c compares the drop in accuracy versus the word length in characters, showing a positive correlation.
This result suggests that longer words are generally easier for our model to attack, possibly because they have more temporal structure to predict.
Which words are guarded by our attack?
Figure 6d displays a histogram of the drop in accuracy for the top and bottom 50 words.
The word “remembered” experiences the most signiﬁcant change in accuracy, nearly becoming unrecognizable in our dataset.
On the other hand, some shorter words, such as “held” or “often”, are not impacted by our attack.
There are only four out of 1631 cases where our attack improves the recognition rate of the word.
4. R EAL R OOM E FFECTIVENESS
Although our attack is optimized without factoring in the room impulse response function, we ﬁnd it generalizes well to real-world settings.
We record a person speaking in two different areas, a workspace and a kitchen.
As a person speaks, there will be reverberation and ambient noise in addition to our attack.
To understand how this impacts our approach, we played the attack through speakers in the same room.
A third-party device consequently receives the sum of the attack, the speech, and the ambient noise convolved with the room impulse response function.
Figure 7 shows a few examples of our attack in acoustic environments.
E THICAL C ONSIDERATIONS
Our research is founded on ethical considerations.
We are excited about the potential for automatic speech recognition to push the frontier of technology, such as in human-computer interaction, telecommunications, accessibility and education.
We are also keenly aware that there can be negative consequences from the deployment of machine learning models in practice.
Notably, automatic speech recognition systems have raised concerns with respect to privacy.
Our method is designed to protect user privacy, and return the control of user speech data back to users.
One potential limitation of our approach is that it is trained on Western speech data and may not generalize to other cultures that are linguistically and phonetically different.
The method has also not been validated on different languages or people with speech impediments.
As such, the dataset and results are not representative of the population.
Deeper understanding of this issue requires future studies in tandem with linguistic and socio-cultural insights.
Another potential adverse consequence is that our model is not 100% accurate, and people may rely on it when it might not be.
For example, the model has not been validated on the full variety of automatic speech recognition systems.
We acknowledge this limitation. The authors attest that they have reviewed the ICLR Code of Ethics and we acknowledge that this code applies to our submission.
We are committed in our work to abide by the ethical principles from the ICLR Code of Ethics.
A A PPENDIX A. G LOSSARY OF V ARIABLES Basics
x t
The waveform from time 0 to time t ˆ y t
The estimated speech transcription given x t , assuming no attack ¯ y t
The estimated speech transcription given x t , assuming our attack y t
The ground truth speech transcription for x t
Timing
δ
The maximum amount of time our attack will take to compute r
The duration of the attack waveform t + δ
Given x t , the earliest time our attack can begin under real-time constraints t
+ δ + r
Given x t , the time our attack will end under real-time constraints
Attacks α t
The attack that ﬁnishes playing at time t α t + δ + r
The attack that ﬁnishes playing at time t + δ + r (redundant with above) x t + δ + r + α x + δ + r
The mixed signal received by an eavesdropper (cid:15)
The (cid:96) ∞ norm bound on the attack
Models f ψ ( · )
The ASR neural network with parameter vector ψ g θ ( · )
Our predictive attack model with parameter vector θ
L The loss function (CTC loss) A. N ETWORK A RCHITECTURE The architecture is comprised of 8 down-sampling convolutional blocks, followed by 4 up-sampling convolutional blocks, followed by a linear layer.
The down-sampling convolutional block is comprised of a reﬂection padding, followed a 2d convolution layer, followed by a 2d batch norm, followed by a prelu activation function.
The ﬁrst downsampling block has 64 channels, the second through the seventh have 128, and ﬁnally the last one has 256 channels.
The last conv block also has a leaky relu instead of prelu.
Here the signal is reshaped into a one-hot vector.
The upsampling blocks are comprised of 1-dimensional ConvTranspose 1d, and a leaky relu activation function.
The ﬁrst has 64 channels, the second 32, the third 16, the fourth, 1.
Finally, the linear layer is followed by a tanh activation function.
A. R ETRAINED D ELAY
While 6 plots the resulting WER as we shift the delay δ on a ﬁxed trained network, here we retrain a new network be δ .
As expected, the larger the δ , the lower the WER.
[Figure]