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Hound voice search NLP demo [video]
- iLoch 11y agoHmm... When your tech works this well, do you want to get acquired? This reminds me of Pied Piper in the way that it's just so much better than anything else out there. Applications for something like this stretch beyond a personal assistant; combining it with something like Watson or WolframAlpha could be very useful. I feel like I could actually use this to control my computer with confidence, for example.
- jdiez17 11y agoThey seem to have (plans for) a way[1] to integrate this into other applications. Looks like it would reduce a lot of the friction that exists with current-gen voice recognition systems. [1] https://www.houndify.com/ https://www.houndify.com/
- hackersssss 11y agothis already exists --> api.ai
- codeshaman 11y ago"What the fuck was that ??" were the exact words I spoke out loud after I saw this video. Is this for real, no editing, no time compression ? Does it really understand those questions or is it preprogrammed ? Looks kind of incredible to be true. If it is, though, I'm in awe.
- Sven7 11y agoI am impressed with the speed. But it says internal demo for good reasons I am assuming :)
- christop 11y agoIt worked for me with "what's the capital of the country where the Brandenburg Gate is", and "what's the population of the country with the Eiffel Tower", then "what's the current time there?" Similarly, the restaurant and hotel demos from the other promo video worked fine. Also with follow-up questions like "and what about ones with free wifi?"
- jdiez17 11y agoI am very impressed. The voice recognition seems significantly faster and more accurate than Google's. The interactive back and forth with the mortgage calculations was the coolest part, I think. How are you able to access population and location data that quickly? It feels like it must be stored locally.
- spyder 11y ago"significantly faster and more accurate than Google's" Yes it's impressive, but it could be because Google has a lot more users.
- jdiez17 11y agoGoogle is in the business of making things go very fast. I don't think the primary bottleneck in Google's speech recognition is the server load, surely they can add more processing power if that were the issue.
- afsina 11y agoWell, it is actually very demanding. ASR systems usually work with the speed of 1 RT (RT= real time factor, meaning recognizing 1 second of speech in 1 second). Approximately %60-70 of these processing goes to acoustic scoring. Rest is search in a large sub-phonetic+words graph and feature extraction (feature extraction takes a tiny percentage actually). Nowadays acoustic scoring is done by large deep neural networks. And they are quite computation intensive. One can use GPU for that and indeed it works really fast if you have all the speech beforehand (off-line or batch mode). But for live recognition, GPUs lose their advantage quite a bit. Probably that is why Google worked on quantized vectorization and other tricks to make the DNNs fast in CPU [1]. I am quite sure this creates an immense pressure on their servers when tens of thousands of concurrent speech streams are queued for recognition. Perhaps todays GPUs are better in that aspect and more work can be delegated to decrease the pressure. There were other interesting work which utilizes almost all processing in GPU [2]. In short, ASR systems are very very processing hungry and a challenge for everyone, probably even for Google. [1] http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/37631.pdf http://static.googleusercontent.com/media/research.google.co... [2] http://www.cs.cmu.edu/~ianlane/hydra/#&panel1-1 http://www.cs.cmu.edu/~ianlane/hydra/#&panel1-1
- nerdy 11y agoThe voice recognition and speech parsing is incredible. Combining it with some calculations and interactivity is over the top!
- tux1968 11y agoLooks good. Bit frustrating that it's not available in Canada though.
- IshKebab 11y agoOr the UK though. I wish Google Play had a "Download Anyway" button. Region blocking is retarded.
- froo 11y agoIncoming acquisition in 3...2.....1
- deleted 11y ago[deleted]
- tomp 11y agoThey advertise it as "speech-to-meaning". I've been thinking about meaning in AI and how important it is for understanding and interacting with the world to be able to answer "What does this mean?" and "Does this make sense?" Does anyone hove any insight or references to recent research about how to model, train for and represent meaning?
- jdiez17 11y agoCheck this[1] out: it describes some techniques used to convert natural language to machine-readable descriptions. [1] https://github.com/wiseman/energid_nlp https://github.com/wiseman/energid_nlp
- nl 11y agoHere's a chronological reading list of techniques Traditional NLP -> 2012, IBM Watson papers[1] Watson is probably the pinnacle of "traditional" style NLP (ie, Tokenization/Lemmatization/Part of Speech Tagging/Framing/Knowledge Engineering/etc) Word2Vec -> 2013, Google paper[2] Word2Vec kind of exploded everyone's mind for a while. Subgraph Embedding -> 2015, Facebook AI Group [3][4] [1] http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=6177717 http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=617771... [2] http://arxiv.org/pdf/1301.3781.pdf http://arxiv.org/pdf/1301.3781.pdf [3] https://research.facebook.com/publications/1473550739586509/question-answering-with-subgraph-embeddings/ https://research.facebook.com/publications/1473550739586509/... [4] http://arxiv.org/abs/1502.05698 http://arxiv.org/abs/1502.05698
- syllogism 11y agoYou might be interested to read this: http://arxiv.org/pdf/1402.3722v1.pdf http://arxiv.org/pdf/1402.3722v1.pdf word2vec Explained: Deriving Mikolov et al.’s Negative-Sampling Word-Embedding Method. Yoav Goldberg and Omer Levy. arXiv 2014. [pdf] The word2vec software of Tomas Mikolov and colleagues has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks language-modeling crowd, we had to struggle quite a bit to figure out the rationale behind the equations.
- Neurocynic 11y agoGoogle play link - https://play.google.com/store/apps/details?id=com.hound.android.app https://play.google.com/store/apps/details?id=com.hound.andr... apkmirror link - http://www.apkmirror.com/apk/soundhound-inc/hound/soundhound-hound-1-0-0-beta-android-apk-download/ http://www.apkmirror.com/apk/soundhound-inc/hound/soundhound... Edit: Currently, only US devices are supported. You can sideload using the apk link. In either case, you need an invite code for which you can register from in app or on the website - www.soundhound.com/
- nly 11y agoGoogle Play won't let me install it, saying it's incompatible with my Nexus 4 (on Lollypop)
- mrkmcknz 11y agoIf this is indeed a non scripted and uncompressed demo this technology is pretty outstanding. SoundHound[1] are behind this product. [1] http://www.soundhound.com/houndify http://www.soundhound.com/houndify
- rasz_pl 11y agoHope its real. Reminds me those cool expert system demos we say in the eighties, they had all the answers ... for selected narrow group of questions with very specific semantics.
- nl 11y agoWhat is it that impresses people here? The voice understanding is impressive, but obviously a video can show the best-case. The factual questions themselves aren't hard. I've written a toy QA system, and it could handle the base case of those - they are just straight querying on Freebase/DBpedia. The longer question ("the population, land area and capitals of Japan, India and China") was good. If anyone has a working copy, I'd like to know which of these it can handle: "Who is Bill Clinton?" "Who is Bill Clinton's daughter?" "Who did Bill Clinton's daughter marry?" If it can get the third level then it's pretty good. From memory something like OpenEphyra can sometimes get that 3rd level, but usually fails. I thought the contextual querying (where one query led to another and it had to remember the previous details) was pretty good.
- christop 11y agoThe first question returns Wikipedia info, the second two just say "Showing search results for: ...".
- nl 11y agoThanks. So that indicates that the natural language parsing isn't super advanced. "Traditional"-style dependency parsing should be able to solve that, and some of the Facebook AI group's recent work indicates that neural-network based parsing should be able to handle it too. My toy QA system can handle the second case, without even doing dependency parsing. It's not clear to me if this is a problem or not. If their voice recognition is as good as it seems that's pretty significant technology.
- iamcasen 11y agoSoundhound programmer here: We just haven't developed that domain of knowledge yet. The natural language part of things can absolutely understand that, but the underlying base of understanding hasn't been developed. For example, we haven't done movie show times yet either. Doesn't mean the natural language parsing isn't advanced ;)
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- WhitneyLand 11y agoI don't believe it will be exactly this good in everyday usage, if for no other reason than speed. Even if the parsing is done locally, broad data queries will have to hit the cloud.
- beenpoor 11y agoVery impressive!
- kolev 11y agoHow can I cut in line and get an invite? I find it a bit unfair that those who supported SoundHound didn't get any special treatment.