Learning Word-Like Units from Joint Audio-Visual Analylsis
Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the words ``lighthouse** within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.
This paper is a follow-up on the NIPS 2016 paper *Unsupervised learning of spoken language with visual context*, and does exactly what that paper proposes in its future work section: *to perform acoustic segmentation and clustering, effectively learning a lexicon of word-like units* using the embeddings that their system learns. The analysis is very interesting and I really like where the authors are going with this. My main concern is novelty. It feels like this work is a rather trivial follow-up on an existing model, which is fine, but then the analysis should be more satisfying: currently, it feels like the authors are just illustrating some of the things that the NIPS model (with some minor improvements) learns. For a more interesting analysis, I would have liked things like a comparison of different segmentation approaches (both in audio and in images), i.e., suppose we have access to the perfect segmentation in both modalities, what happens? It would also be interesting to look at what is learned with the grounded representation, and evaluate e.g. on multi-modal semantics tasks. Apart from that, the paper is well written and I really like this research direction. It is very important to analyze what models learn, and this is a good example of the types of questions one should ask. I am afraid, however, that the model is not novel enough, nor the questions deep enough, to make this paper better than borderline for ICLR.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Word-Like Units from Joint Audio-Visual Analylsis
Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the words ``lighthouse** within an utterance and associate them with image regions containing lighthouses. We do not use any form of conventional automatic speech recognition, nor do we use any text transcriptions or conventional linguistic annotations. Our model effectively implements a form of spoken language acquisition, in which the computer learns not only to recognize word categories by sound, but also to enrich the words it learns with semantics by grounding them in images.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper is a follow-up on the NIPS 2016 paper *Unsupervised learning of spoken language with visual context*, and does exactly what that paper proposes in its future work section: *to perform acoustic segmentation and clustering, effectively learning a lexicon of word-like units* using the embeddings that their system learns. The analysis is very interesting and I really like where the authors are going with this. My main concern is novelty. It feels like this work is a rather trivial follow-up on an existing model, which is fine, but then the analysis should be more satisfying: currently, it feels like the authors are just illustrating some of the things that the NIPS model (with some minor improvements) learns. For a more interesting analysis, I would have liked things like a comparison of different segmentation approaches (both in audio and in images), i.e., suppose we have access to the perfect segmentation in both modalities, what happens? It would also be interesting to look at what is learned with the grounded representation, and evaluate e.g. on multi-modal semantics tasks. Apart from that, the paper is well written and I really like this research direction. It is very important to analyze what models learn, and this is a good example of the types of questions one should ask. I am afraid, however, that the model is not novel enough, nor the questions deep enough, to make this paper better than borderline for ICLR.
assistant
This paper is a follow-up on the NIPS 2016 paper *Unsupervised learning of spoken language with visual context*, and does exactly what that paper proposes in its future work section: *to perform acoustic segmentation and clustering, effectively learning a lexicon of word-like units* using the embeddings that their system learns. The analysis is very interesting and I really like where the authors are going with this. My main concern is novelty. It feels like this work is a rather trivial follow-up on an existing model, which is fine, but then the analysis should be more satisfying: currently, it feels like the authors are just illustrating some of the things that the NIPS model (with some minor improvements) learns. For a more interesting analysis, I would have liked things like a comparison of different segmentation approaches (both in audio and in images), i.e., suppose we have access to the perfect segmentation in both modalities, what happens? It would also be interesting to look at what is learned with the grounded representation, and evaluate e.g. on multi-modal semantics tasks. Apart from that, the paper is well written and I really like this research direction. It is very important to analyze what models learn, and this is a good example of the types of questions one should ask. I am afraid, however, that the model is not novel enough, nor the questions deep enough, to make this paper better than borderline for ICLR.