Likelihood to Recommend My job requires that I produce lengthy and detailed minutes of meetings and Nuance Dragon Speech Recognition is absolutely ideally suited for this purpose. Notably, meetings are recorded and it is extremely easy to playback the recording of meetings while dictating notes. This is a remarkable saving in time and effort in producing minutes that might otherwise take a few days. I cannot think of any scenario where it would be less appropriate to use Nuance Dragon Speech Recognition other than in a situation where it is not possible to dictate for whatever reason.
Read full review TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
Read full review Pros The main advantage is that it calibrates itself around your tone and accent. Integration with many apps and operative system is good. Integrating custom words and acronyms is very easy, so, over time, recognition becomes very precise and customized. Read full review A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc. Amazing community helps developers obtain knowledge faster and get unblocked in this active development space. Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models. Read full review Cons Word recognition can always improve. Sometimes puts stray words or characters on the page if you forget to tell the microphone to go to sleep. Adds non-spoken words for no reason if there is a delay in speaking. Read full review RNNs are still a bit lacking, compared to Theano. Cannot handle sequence inputs Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time. Read full review Likelihood to Renew Nuance Dragon Speech Recognition is not up to par with any of the other AI voice recognition technologies currently available The software is also very buggy and crashes frequently Punctuation also seems like an afterthought for Nuance Dragon Speech Recognition software--requiring the user to speak out each comma, period, etc. Read full review Usability Once you learn the product, it is quite easy to use.
Read full review Support of multiple components and ease of development.
Read full review Support Rating One of the worst companies in terms of customer service getting the download file because of a laptop upgrade took me months of work. No jokes.
Read full review Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
Read full review Implementation Rating Use of cloud for better execution power is recommended.
Read full review Alternatives Considered Other than the more recent speech recognition tools from Microsoft, Google, etc., I have always used Nuance Dragon Speech Recognition. I was introduced to AI technology on an appraisal assignment. During the engagement, I had an opportunity to learn about the technology, and when I researched speech recognition software, the best reviews were of Nuance Dragon Speech Recognition. I purchased Nuance Dragon Speech Recognition and have stayed with the product.
Read full review Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features,
Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
Read full review Return on Investment Nuance Dragon Speech Recognition has made it easier to dictate progress notes Nuance Dragon Speech Recognition has made seeing patients more efficient Nuance Dragon Speech Recognition was a little expensive for our smaller practice Read full review Learning is s bit difficult takes lot of time. Developing or implementing the whole neural network is time consuming with this, as you have to write everything. Once you have learned this, it make your job very easy of getting the good result. Read full review ScreenShots