Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Characterization of the representations learned in intermediate layers of deep networks can provide valuable insight into the nature of a task and can guide the development of well-tailored learning strategies.
Here we study convolutional neural network-based acoustic models in the context of automatic speech recognition.
Adapting a method proposed by Yosinski et al. [2014], we measure the transferability of each layer between German and English to assess the their language-specifity.
We observe three distinct regions of transferability: (1) the first two layers are entirely transferable between languages, (2) layers 2–8 are also highly transferable but we find evidence of some language specificity, (3) the subsequent fully connected layers are more language specific but can be successfully finetuned to the target language.
To further probe the effect of weight freezing, we performed follow-up experiments using freeze-training [Raghu et al., 2017].
Our results are consistent with the observation that CCNs converge 'bottom up' during training and demonstrate the benefit of freeze training, especially for transfer learning.
The acoustic properties of speech vary across languages.
This is evidenced by the fact that monolingual acoustic models (AMs) are the de facto standard in automatic speech recognition (ASR), while multi-lingual AMs are an active area of development BID2 BID3 BID4 BID5 .
Requiring large amounts of training data to build separate AMs for every language is a barrier to successful ASR systems for low-resource languages.
Ideally, AMs would be designed to strategically leverage off-task data as much as possible.
AMs often take the form of a deep network which learns to map from acoustic features to context-dependent phones in a language-specific phone set.
It is not clear how exactly this transformation is performed or what is represented in the intermediate layers of such networks.
Better characterization of the intermediate representations of AMs may help to guide data-efficient training procedures.
Similar characterizations of networks trained on visual tasks have inspired new transfer learning procedures.
For example, BID0 characterized the task specificity at each layer of a network trained on ImageNet using transferability as a proxy for task-specificity.
This characterization motivated Adaptive Transfer Networks BID6 where parts of a network are trained on the source domain while other parts of the network are finetuned, or adapted, to the target domain, preserving the limited target data for learning highly task-specific parameters.
Similar adaptive transfer learning procedures may also prove to be useful for building AMs for data-poor languages.
However, the exact shape of the transition from task-general to task-specific representations in deep network-based AMs is unknown.Much of the previous work on characterizing intermediate layers of deep networks has focused on relatively solvable tasks in the visual domain (e.g. hand written digit recognition, visual object recognition).
Few studies have characterized the intermediate representations of networks trained on acoustic tasks BID7 BID8 BID9 , which, in practice, are not always trained long enough to converge completely (test error still slowly decreasing at the end of training) due to the long training time required.
It is not clear to what extent existing methods developed to probe networks trained on visual tasks will be applicable and useful to study networks that may be underfitting on difficult acoustic tasks.Here we studied convolutional neural networks (CNNs) used for acoustic modeling in ASR systems.
We characterized the language-specificity of each layer across languages using an approach inspired by BID0 .
Subsets of a network trained on one language were "implanted" into another network which was trained on a second language.
The effect of the implant on performance indicated the language-specificity of the features in the implant.
Our main contribution is the characterization of the language-specificity of intermediate layers of CNN-based acoustic models.
Additionally, we demonstrate the adaptation of an analysis method originally designed to probe visual networks to study networks in an underfitting regime on a phone classification task.
Our results suggest that, despite a large degree of transferability of intermediate acoustic features between languages, naive approaches to transfer (e.g. initializing with parameters from another language) are not the most efficient.
In particular, early layers need not be finetuned on the target language at all.
Subsequent layers benefit greatly from freeze training on the target language.
These freeze trained transfer networks outperform networks trained solely on the target language, which demonstrates the improved generalization that can be achieved when incorporating data from multiple sources.The performance of the networks with finetuning is largely consistent with BID0 .
However, the performance of networks without finetuning deviates considerably.
The transfer networks without finetuning in BID0 show a gradual drop in performance, starting at the 4th convolutional layer and eventually dropping nearly 8 pp by the penultimate layer (see FIG1 from BID0 ).
Our transfer networks without finetuning, on the other hand, show a sharp drop in performance that starts only at the first fully connected layer (layer 9).
For the selfer networks without finetuning, we did not observe a performance drop when networks were chopped at middle layers, as was reported in BID0 .
Instead, our selfer networks without finetuning outperformed all other models, with accuracy increasing nearly monotonically with the depth at which the network was chopped.
BID0 's experiments with random weights quickly drop to near-chance performance by layer 3, whereas our networks with random weights decline gradually with depth, only approaching near-chance performance when all but the last layer are random.The success of our selfer networks without finetuning is at least partly explained by the fact that we are in an underfitting regime. Unlike in BID0 , our baseline model has not converged completely and we would expect continued training to improve performance. However, if that were the only factor at play, then we would expect our selfer networks with finetuning to also improve but they do not. Something about freezing all but the last layer(s) facilitates a~3 pp improvement over baseline in the selfer but not the transfer networks. This suggests that there is some important language-specific information in the layers that show a difference between the selfer and transfer networks without finetuning (layer 3+). Layers 10 and 11 show worse than baseline performance for the transfer network without finetuning, indicating a larger degree of language-specificity in these representations.Our freeze training results corroborate the interpretation that weight freezing is responsible for the success of our selfer networks without finetuning. Furthermore, our freeze-trained transfer networks performed best overall, demonstrating that freeze training can actually recover the language-specific information lacking in our transfer networks without finetuning, yielding improved generalization. This likely reflects the observation from BID1 that CNNs converge 'bottom-up' during training, with early layers stabilizing earlier in training.
Relatedly, BID14 state the proposition that no intermediate layer of a multi-layer neural network will contain more target-related information than the raw input, which requires a 'bottom-up' flow of information; intermediate layers cannot pass on target-related information that they do not receive.
Thus we conclude that freezing the weights of a given layer can improve performance iff that layer already passes on the target-related information in a representation that can be disentangled by subsequent layers.
This was not generally the case in our transfer chimera networks because important language-specific information was not being conveyed.
The progressive freeze training regime, proposed by BID1 , allowed this important language-specific information to be learned, whereas generic fine-tuning did not.
In this way, making fewer parameter updates actually led to significant performance gains.
This may be partly explained by the fact that smaller networks train faster BID15 .
Perhaps generic fine-tuning would eventually achieve the same accuracy, but after many more iterations.
All but the first two layers of our CNNs based acoustic models demonstrated some degree of language-specificity but freeze training enabled successful transfer between languages.