2020 – Real-time captioning by groups of non-experts

March 4, 2020 Subil Abraham Leave a comment

This paper pioneers the approach of using crowd work for closed captioning systems. The scenario they target is classes and lectures, where a student can hold up their phone and record the speaker and the sound the transmitted to the crowd workers. The sound that is passed is given as bite sized pieces for the crowd workers to transcribe, and the paper’s implementation of the multiple sequence alignment algorithms takes those transcriptions and combines them. The focus of the tool is very much on real-time captioning so the amount of time a crowd worker can spend on a portion of sound is limited. The authors design interfaces on the worker side to promote continuous transcription, and on the user side to allow them to correct the received transcriptions in real time, enhancing the quality further. The authors had to deal with interesting challenges in resolving errors in the transcription, which they did by a combination of comaparing transcriptions of the same section from different crowd workers, using bigram and trigram data to validate the word ordering. Evaluations showed that precision was stable while coverage increased with increase in the number of workers, while having lower error rate compared to automatic transcription and untrained transcribers.

One thing that needs to be pointed out about this work is that I believe that ASR is always rapidly improving and has made significant strides from when this paper was published. From my own anecdotal experience, Youtube’s automatic closed captions are getting very very close to being fully accurate (however, thinking back on our reading of the Ghost Work book at the beginning of the semester, I wonder if Youtube is cheating a bit and using crowd work intervention for some their videos to help their captioning AI along). I also find that the author’s solution for merging the transcriptions of the different sound bites is interesting. How they would solve that was the first thing that was on my mind because it was not going to be a matter of simply aligning the time stamps because those were definitely going to be imprecise. So I do like their clever multi part solution. Finally, I was a little surprised and disappointed that the WER was at ~45% which was a lot higher than I expected. I was expecting the error rate to be a lot closer to professional transcribers but unfortunately not. The software still has a way to go in that.

How could you get the error rate down to the professional transcriber’s level? What is going wrong there that is causing it to be that high?
It’s interesting to me that they couldn’t just play isolated sound clips but instead had to raise and lower volume on a continuous stream for better accuracy. Where are the other places humans work better when they have a continuous stream of data rather than discrete pieces of data?
Is there an ideal balance between choosing precision and coverage in the context of this paper? This was something that also came up in last week’s readings. Should the user decide what the balance should be? How would they do it when there can be multiple users all at the same location trying to request captioning for the same thing?

Subil Abraham – 03/04/2020 – Pull the Plug

March 4, 2020 Subil Abraham 1 Comment

The paper proposes a way of solving the issue of deciding when a computer or human should do the work of foreground segmentation of images. Foreground segmentation is a common task in computer vision where the idea is that there is an element in an image that is the focus of the image and that is what is needed for actual processing. However, automatic foreground segmentation is not always reliable so sometimes it is necessary to get humans to do it. The important question is deciding which images you send to humans for segmentation because hiring humans are expensive. The paper proposes a machine learning method that calculates the quality of a given coarse or fine grained segmentation and decide if it is necessary to bring in a human to do the segmentation. They evaluate their framework by examining the quality of different segmentation algorithms and are able to acheive the quality equivalent to 100% human work by using only 32.5% human effort for Grab Cut segmentation, 65% human effort for Chan Vese, and 70% human effort for Lankton.

The authors have pursued a truly interesting idea in that they are not trying to create a better way of automatic image segmentation, but rather creating a way of determining if the auto image segmentation is good enough. My initial thought was couldn’t something like this be used to just make a better automated image segmenter? I mean, if you can tell the quality, then you know how to make it better. But apparently that’s a hard enough problem that it is far more helpful to just defer to a human when you predict that your segmentation quality is not where you want it. It’s interesting that they talk about pulling the plug on both computers and humans but the focus of the paper seems to be focused on pulling the plug on computers i.e. the human workers are the backup plan in case the computer can’t do the quality work and not the other way around. This applies to both their cases, coarse grained and fine grained segmentation work. I would like to see future work where the primary work is done by humans first and then test to see how pulling the plug on the human work would be effective and where the productivity would increase. This would have to be work in something that is purely in the human domain (i.e. can’t use regular office work because that is easily automatable).

What are examples of work where we pull the plug on the human first, rather pulling the plug on computers?
It’s an interesting turn around that we are using AI effort to determine quality and decide when to bring humans in, rather than improving the AI of the original task itself. What other tasks could you apply this, where there are existing AI methods but an AI way of determining quality and deciding when to bring in humans would be useful?
How would you set up a segmentation workflow (or another application’s workflow) where when you pull the plug on the computer or human, you are giving the best case result to the other for improvement, rather than starting over from scratch?

03/04/2020 – Pull the Plug? Predicting If Computers or Humans Should Segment Image – Yuhang Liu

March 4, 2020 yuhang Liu 1 Comment

Summary:

This paper examines a new image segmentation method. Image segmentation is a key step in any image analysis task. There have been many methods before, including low-efficiency manual methods and automated methods that can produce high-quality pictures, but these methods have certain disadvantages. The authors therefore propose a distribution framework that can predict how best to assign fixed labor to collect higher quality segmentation for a given image and automated method. Specifically, the author has implemented two systems, which can perform the following processing on images when doing image segmentation:

Use computers instead of humans to create the rough segmentation needed to initialize the segmentation tool,
Use computers to replace humans to create the final fine-grained segmentation. The final experiments also proved that relying on this hybrid, interactive segmentation system can achieve faster and more efficient segmentation.

Reflection:

Once, I did a related image recognition project. Our subject is a railway turnout monitoring system based on computer vision, which is to detect the turnout of the railroad track from the picture, and the most critical step is to separate the outline of the railroad track. At that time, we only using the method of computer separation, the main problem we encountered at the time was that when the scene became complicated, we would face to complex line segments, which would affect the detection results. As mentioned in this paper, using human-machine, the combined method can greatly improve the accuracy rate. I very much agree with it, and hope that one day I can try it myself. At the same time, what I most agree with is that the system can automatically assign work instead of all photos going through a same process. For a photo, only the machine can participate, or artificial processing is required. This variety of interactive methods, It is far more advantageous than a single method, which can greatly save workers’ time without affecting accuracy, and the most important point is that complex interaction methods can adapt to process more diverse pictures. Finally, I think similar operations can be applied to other aspects. This method of assigning tasks through the system can coordinate the working relationship between humans and machines, for example, in other fields, such as sound sentiment analysis and musical background separation. In these aspects, humans have the incomparable advantages of machines and can achieve good results, but it takes a long time and is very expensive. Therefore, if we can classify this kind of thinking, deal with the common working relationship between humans and machines, and give complex situations to people or pass the rough points of the machine first, then the separation cost will be greatly reduced, and the accuracy rate will not be affected, so I It is believed that this method has great application prospects, not only because of the many application directions of image separation, but we can also learn from this idea to complete more detailed analysis in more fields.

Question:

Is this idea of cooperation between man and machine worth learning?
Because the system defines the working range of people and machines, will the machine reduce the accuracy due to the results of human work?
Does man-machine cooperation pose new problems, such as increasing costs?

03/04/20 – Akshita Jha – Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind

March 4, 2020 Akshita Jha 1 Comment

Summary:
“Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind” by Salisbury et. al. talks about the important problem of accessibility. The authors talk about the challenges that arise from an automatic image captioning system and how the imperfections in the system may hinder a blind person’s understanding of social media posts that have embedded imagery. The authors use mixed methods to evaluate and subsequently modify the captions generated by the automated system for images embedded in social media posts. They study how crowdsourcing can enhance the existing workflows and that provide scalable and useful alt text for the blind. The imperfections of the current automated captioning system hinder the user’s understanding of an image. The authors do a detailed analysis of the conversations collected by them to design user-friendly experiences that can effectively assist blind users. The authors focus on three research questions: (i) What value is provided by a state-of-the-art vision-to-language API in assisting BVI users, and what are the areas for improvement? (ii) What are the trade-offs between alternative workflows
for the crowd assisting BVI users? (iii) Can human-in-the loop workflows result in reusable content that can be shared with other BVI users? The authors study varying levels of human engagements and automated systems to come up with a final system that better understands the requirements for creating good quality al-text for blind and visually impaired users.

Reflections:
This is an interesting work as it talks about the often ignored problem of accessibility. The authors focus on images embedded in social media posts. Most of the times the automatic captions given by an automated system trained using a machine learning algorithm are inadequate and non descriptive. This might not be so much of a problem for day to day users but can be a huge challenge for blind people. This is a thoughtful analysis done by the authors keeping accessibility in mind. The authors validate their approach by running a follow-up study with seven blind and visually impaired users. The users were asked to compare the uncorrected vision to language caption and the alt text provided by their system. The findings showed that the blind and visually impaired users would prefer the conversational system designed by the authors to better understand the images. However, if the authors had taken the feedback from the target user group while developing the system that would have been more helpful instead of just asking the users to test the system. Also, the tweets used by the authors might not be representative of the kinds of tweets in the target users’ timeline.

Questions:
1. What do you think about the approach taken by the authors to generate the alt-text?
2. Would it have been helpful to conduct a survey to understand the needs of the blind and visually impaired users before developing the system?
3. Don’t you think using a conversational agent to understand the image embedded in tweets is too cumbersome and time consuming?

03/04/2020 – Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Reﬁning Vision-to-Language Technology for the Blind – Yuhang Liu

March 4, 2020 yuhang Liu 1 Comment

Summary:

The authors of this paper explored that visually impaired users are limited by the availability of suitable alternative text when accessing images in social media. The author believes that the beneficial of those new tools that can automatically generate captions are unknown to the blind. So through experiments, the authors studied how to use crowdsourcing to evaluate the value provided by existing automation methods, and how to provide a scalable and useful alternative text workflow for blind users. Using real-time crowdsourcing, the authors designed crowd-interaction experiments that can change the depth. These experiments can help explain the shortcomings of existing methods. The experiments show that the shortcomings of existing AI image captioning systems often prevent users from understanding the images they cannot see , And even some conversations can produce erroneous results, which greatly affect the user experience. The authors carried out a detailed analysis and designed a design that is scalable, requires crowdsourced workers to participate in improving the display content, and can effectively help users without real-time interaction.

Reflection:

First of all, I very much agree with the author’s approach. In a society where the role of social networks is increasingly important, we really should strive to make social media serve more people, especially for the disadvantaged groups in our lives. The blind daliy travel inconveniently, social media is their main way to understand the world, so designing such a system would be a very good idea if it can help them. Secondly, the author used the crowdsourcing method to study the existing methods. The method they designed is also very effective. As a cheap human resource, the crowdsourcing method can test a large number of systems in a short time, but I think this method There are also some limitations. It may be difficult for these crowdsourced workers to think about the problem from the perspective of the blind, which makes their ideas, although similar to the blind, not very accurate, so there are some gaps of the results with blind users. Finally, I have some doubts about the system proposed by the author. The authors finally proposed a workflow that combines different levels of automation and human participation. This shows that this interaction requires the participation of another person, so I think this interaction There are some disadvantages to this method. Not only will it cause a certain delay, but because it requires other human resources, it also requires some blind users to pay more. I think the ultimate direction of development should be free from human constraints, so I think we can compare the results of workers with the original results and let machine learning. That is to use the results of crowdsourcing workers for machine learning. I think it can reduce the cost of the system while increasing the efficiency of the system, and provide faster and better services for more blind users.

Question：

Do you think there is a better way to implement these functions, such as studying the answers of workers, and achieving a completely automatic display system?
Are there some disadvantages to using crowdsourcing platforms?
Is it better to change text to speech for the visually impaired?

03/04/20 – Lulwah AlKulaib- CrowdStreetView

March 4, 2020 Lulwah AlKulaib 1 Comment

Summary

The authors try to assess the accessibility of sidewalks by hiring AMT workers to analyze Google Street View images. Traditionally, sidewalk assessment is conducted in person via street audits which are highly labor intensive and expensive or by reporting calls from citizens. The authors propose using their system as an alternative for a proactive solution to this issue. They perform two studies:

A feasibility study (Study 1): examines the feasibility of the labeling task with six dedicated labelers including three wheelchair users
A crowdsourcing study (Study 2): investigates the comparative performance of turkers

In study 1, since labeling sidewalk accessibility problems is subjective and potentially ambiguous, the authors investigate the viability of labeling across two groups:

Three members of the research team
Three wheelchair users – accessibility experts

They use the results of study 1 to provide ground truth labels to evaluate crowdworkers performance and to get a baseline understanding of what labeling this dataset looks like. In study 2, the authors investigate the potential of using crowd workers to perform the labeling task. They evaluate their performance on two levels of labeling accuracy:

Image level: tests for the presence or absence of the correct label in an image
Pixel level: examines the pixel level accuracies of the provided labels

They show that AMT workers are capable of finding accessibility problems with an accuracy of 80.6 % and determining the correct problem type with an accuracy of 78.3%. They get better results when using majority voting as a labeling technique 86.9% and 83.9% respectively. They collected 13,379 labels, 19,189verification labels from 402 workers. Their findings suggest that crowdsourcing both the labeling task and the verification task leads to a better quality result.

Reflection

The authors have selected experts in the paper as wheelchair users, when in real life they’re civil engineers. I wonder how that would have changed their labels/results. Since accessibility in the street is not only for wheelchair users. It’s worth investigating by using a pool of multiple experts.

I also think that selecting the dataset of photos to work on was a requirement for this labeling system, else it would have been tedious amount of work on “bad” images. I can’t imagine how this would be a scalable system on google street view as a whole. The dataset requires refinement to be able to label.

In addition, the focal point of the camera was not considered and reduces the scalability of the project. Even though the authors suggest a solution of installing a camera angled towards sidewalks, until that is implemented, I don’t see how this model could work well in the real world (not a controlled experiment).

Discussion

What are improvements that the authors could have done to their analysis?
How would their labeling system work for random Google street view photos?
How would the focal point of the GSV camera affect the labeling?
If cameras were angled towards sidewalks, and we were able to get a huge amount of photos for analysis, what would be a good way to implement this project?

03/04/2020 – Palakh Mignonne Jude – Pull the Plug? Predicting If Computers or Humans Should Segment Images

March 4, 2020 Palakh Mignonne Jude Leave a comment

SUMMARY

The authors of this paper aim to build a prediction system that is capable of determining whether the segmentation of images should be done by humans or computers, keeping in mind that there is a fixed budget of human annotation effort. They focus on the task of foreground object segmentation. They utilized varied domain image datasets such as the Biomedical Image Library with 271 grayscale microscopy images sets, Weizmann with 100 grayscale everyday object images, and Interactive Image Segmentation with 151 RGB everyday object images with the aim of showcasing the generalizability of their technique. They developed a resource allocation framework ‘PTP’ that predicts if it should ‘Plug The Plug’ on machines or humans. They conducted studies on both coarse segmentation as well as fine-grained segmentation. The ‘machine’ algorithms were selected from among the algorithms currently used for foreground segmentation such as Otsu thresholding, Hough transform, etc. The regression model was built using a multiple linear regression model. The 522 images from the 3 data sets mentioned earlier were given to crowd workers from AMT to perform coarse segmentation. The authors found that their proposed system was able to eliminate 30-60 minutes of human annotation time.

REFLECTION

I liked the idea of the proposed system that capitalized on the strengths of both humans and machines and aims to identify when the skill of one or the other is more suited for the task at hand. It reminded me about reCAPTCHA (as highlighted by the paper ‘An Affordance-Based Framework for Human Computation and Human-Computer Collaboration’) that also utilized multiple affordances (both human and machine) in order to achieve a common goal.

I found it interesting to learn that this system was able to eliminate 30-60 minutes of human annotation time. I believe that if such a system were to be used effectively, it would enable developers to build systems faster and ensure that human efforts are not wasted in any way. I thought it was good that the authors attempted to incorporate variety when selecting their data sets, however, I believe that it would have been interesting if the authors had combined these data sets with a few more data sets that contained more complex images (ones with many images that could have been in the foreground). I also liked that the authors have published their code as an open source repository for future extensions of their work.

QUESTIONS

As part of this study, the authors focus on foreground segmentation. Would the proposed system extend well in case of other object segmentation or would the quality of the segmentation and the performance of the system be hampered in any way?
While the authors have attempted to indicate the generalizability of their system by utilizing different data sets, the Weizmann and BU-BIL datasets were grayscale images with relatively clear foreground images. If the images were to contain multiple objects, would the amount of time that this system eliminated be as high? Is there any relation between the difficulty of the annotation task and the success of this system?
Have there any been any new systems (since this paper was published) that attempt to build on top of the methodology proposed by the authors in this paper? What modifications/improvements could be made to this proposed system to improve it (if any improvement is possible)?

03/04/20 – Sukrit Venkatagiri – Toward Scalable Social Alt Text

March 4, 2020 Sukrit Venkatagiri Leave a comment

Paper: Elliot Salisbury, Ece Kamar, and Meredith Ringel Morris. 2017. Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind. In Fifth AAAI Conference on Human Computation and Crowdsourcing.

Summary:
This paper explores a variety of approaches for supporting blind and visually impaired people (BVI) with alt-text captions. They consider two baseline methods using existing computer vision approaches (Vision-to-Language) and Human Corrected Captions. They also considered two workflows that did not depend on CV approaches—TweetTalk conversational workflow, and Structured Q&A workflow. Based on the questions asked from TweetTalk, they generated a set of structured questions to be used in Structured Q&A workflow. They found that V2L performed the worst, and that overall, any approach with CV as a baseline did not perform well. Their TweetTalk conversational approach is more generalizable but also difficult to recruit workers. Finally, they conducted a study of TweetTalk with 7 BVI people and learned that they found it potentially useful. The authors discuss their findings in relation to prior work, as well as the tradeoffs between human-only and AI-only systems, paid v/s volunteer work, and conversational assistants v/s structured Q&A. They also extensively discuss the limitations of this work.

Reflection:
Overall, I really liked this paper and found it very interesting. I think their multiple approaches to evaluating human-AI collaboration was interesting (AI alone, human-corrected, human chat, asynchronous human answers), in addition to the quality perception ratings that were obtained from third party workers. I think this paper makes a strong contribution, but wish they could go into more detail to clarify exactly how the system worked, the different experimental setups, and any other interesting findings that were there. Sadly, there is an 8-page page limit, which may have prevented them from going into more detail.

I appreciate the fact that they built on and used prior work in this paper, namely MacLeod et al. 2017, Mao et al. 2012, and Microsoft’s Cognitive Services API. This way, they did not need to build their own database, CV algorithms, or real-time crowdworker recruiting system. Instead, it allowed them to focus on more high-level goals.

Their findings were interesting. Especially the fact that human-corrected CV descriptions performed poorly. It is unclear how satisfaction is different between the various conditions, for first-party ratings. It may be because users had context through conversation and but was not included in their ratings. The results also show that current V2L systems have worse accuracy than human-in-the-loop approaches. Sadly, there was no significant difference in accuracy between HCC and description generated after TweetTalk, but SQA improved significantly.

Finally, the validation with BVI users is welcome, and I believe more Human-AI work needs to actually work with real users. I wonder how the findings might differ if they were used in a real, social context, or with people on MTurk instead of the researchers-as-workers.

Overall, this was a great paper to read and hope others build on this work, similar to how the authors here have directly leveraged prior work to advance our understanding of human-AI collaboration for alt-text generation.

Questions:

Are there any better human-AI workflows that might be used that the authors did not consider? How would they work and why would they be better?
What are the limitations of CV that led to the findings in this paper that any approach with CV performed poorly?
How would you validate this system in the real world?
What are some other next steps for improving the state of the art in alt-text generation?

03/04/20 – Sukrit Venkatagiri – Pull the Plug?

March 4, 2020 Sukrit Venkatagiri Leave a comment

Paper: Danna Gurari, Suyog Jain, Margrit Betke, and Kristen Grauman. 2016. Pull the Plug? Predicting If Computers or Humans Should Segment Images. 382–391.

Summary:
This paper proposes a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and methods. The framework uses a “pull-the-plug” model, predicting when to use human versus computer annotators. More specifically, the paper proposes a system that intelligently allocates computer effort to replace human effort for initial coarse segmentations. Second, it automatically identifies images to have humans re-annotate by predicting which of the images the automated methods did not segment well enough. This method could be used for a variety of uses cases, and the paper tests it on three datasets and 8 segmentation methods. The findings show that this method significantly outperformed prior work across a variety of metrics, ranging from quality prediction, initial segmentation, fine-grained segmentation, and cost.

Reflection:
Overall, this was an interesting paper to read that is largely focused on performance and accuracy. The paper shows that the methods are superior to prior work and is now the state of the art for image segmentation when it comes to these three datasets, and for saving costs.

I wonder what this paper might have looked like if it was more focused on creativity and innovation, rather than performance and cost-savings. For example, in HCI there are studies of using crowds to generate ideas, solve mysteries, and critique designs. Perhaps this approach might be used in a way that humans and machines can provide suggestions and they build off of each other.

More specifically, related to this paper, I wonder how the results would generalize to datasets other than the three used here, or to real-world examples, for perhaps self-driving cars, etc. Certainly, a lot more work needs to be done, and the system would need to be real-time, meaning human computation might not be a feasible method for self-driving cars. Though, certainly they could be used for generating training dataset for self-driving car algorithms.

This entire approach relies on the proposed prediction module, and it would be interesting to explore other edge cases where the predictions are better made by humans rather than through machine intelligence.

Finally, the finding that the computer segmented images more similarly to experts than crowd workers was interesting, and I wonder why—was it because the computer algorithms were trained on expert-generated training sets? Perhaps the crowd workers would perform better over time or with training. In that case, the results might have been better overall when combining the two.

Questions:

How might you use this approach in your class project?
Where does CV fail and where can humans augment it? What about the reverse?
What are the limitations of a “pull-the-plug” approach, and how can they be overcome?
Where else might this approach be used?

03/04/20 – Fanglan Chen – Real-time Captioning by Groups of Non-experts

March 4, 2020 Fanglan Chen 1 Comment

Summary

Lasecki et al.’s paper “Real-time Captioning by Groups of Non-experts” explores a new approach of relying on a group of non-expert captionists to provide speech captions of good quality, and presents an end-to-end system called LE-GION: SCRIBE which allows collective instantaneous captioning for live lectures on-demand. In the speech captioning task, professional stenographers can achieve high accuracy. However, the manual efforts are very expensive and must be arranged in advance. For effective captioning, the researchers introduce the idea of having a group of non-expects to caption audio and merging their inputs to achieve more accurate captions. Their proposed SCRIBE has two components, one is an interface for real-time captioning designed to collect the partial captions from each crowd worker, and the other is real-time input combiner for merging the collective captions into a single out-put stream in real-time. Their experiments show that proposed solution is feasible and non-experts can provide captioning of good quality and content coverage with short per-word latency. The proposed model can be potentially extended to allow dynamic groups to exceed the capacity of individuals in various human performance tasks.

Reflection

This paper conducts an interesting study of how to achieve better performance of a single task via collaborative efforts of a group of individuals. I think this idea aligns with ensemble modeling in machine learning. The idea presented in the paper is to generate multiple partial outputs (provided by team members and crowd workers) and then use an algorithm to automatically merge all of the noisy partial inputs into a single output. Similarly, ensemble modeling is a machine learning method where multiple diverse models are developed to generate or predict an outcome, either by using multiple different algorithms or using different training data sets. Then the ensemble model aggregates the output of each base model and generates the final output. The motivation for relying on a group of non-expert captionists to achieve better performance beyond the capacity of each non-expert corresponds to the idea of using ensemble models to reduce the generalization error and get more reliable results. As long as the base models are diverse and independent, the performance of the model increases when the ensemble approach is used. This approach also seeks the collaborative efforts of crowds in obtaining the final results. In both approaches, even though the model has multiple human/machine inputs as its sources, it acts and performs as a single model. I would be curious to see how ensemble models perform on the same task compared with the crowdsourcing proposed in the paper.

In addition, I think the proposed framework in the paper may work for general audio captioning. I am wondering how it would perform in regards to domain-specific lectures. As we know, lectures in many domains, such as medical science, chemistry, psychology, etc. are expected to have some terminologies that might be difficult to capture by an individual without the professional background in the field. There would be possible cases that none of the crowd worker can type those terms correctly, which may result in the incorrect caption. I think the paper can be strengthened with a discussion about under what kind of situations the proposed method works best. To continue the point, another possibility is to leverage the advantages of pre-trained speed recognition models and crowd works to develop a human-AI team to achieve desirable performance.

Discussion

I think the following questions are worthy of further discussion.

Would it be helpful if the recruiting process of crowd workers involves the consideration on their backgrounds, especially for some domain-specific lectures?
Although ASR may not be reliable on its own, is it useful leverage it as a contributor to the input of crowd workers?
Is there any other potential to add a machine-in-the-loop component in the proposed framework?
What do you think about the proposed approach compared with the ensemble modeling that merges the outputs of multiple speech recognition algorithms to get the final results?