Chromecast is back on Amazon

 Amazon will once again allow sales of Apple TV and Google Chromecast on its site, after banning them two years ago in an effort to promote its own Fire TV hardware to online shoppers. The addition of Apple TV is not surprising – Amazon and Apple came to an agreement earlier this year that included the launch of the Amazon Prime Video app for Apple TV, and the return of the Apple TV to… Read More
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Taylor Swift’s new app, The Swift Life, is out now

 In the midst of an reputational makeover, Taylor Swift is debuting a brand new app called The Swift Life. The app is a dedicated social network for Swift’s fans, letting them communicate with each other as well as get exclusive pictures, video, news and more direct from T-Swift herself. And they can communicate with Swift, too. Using an ‘extremely rare and valuable Taymoji&#8217… Read More
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Use the Waterfall Project Management Method to Organize Your Life

The tools we typically use for managing personal projects have a major drawback. To-do lists and their variations (like Kanban boards) are fine to track what should be done and (maybe) who should do it. But they aren’t as good at planning out the when. This is the domain of business-oriented tools, such as the Waterfall project management approach. One of the primary reasons companies use a set of tools and processes to manage their projects is for profitability. Once management decides a particular project will add value to the business, there is a cost for every hour that project...

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Improving End-to-End Models For Speech Recognition

Posted by Tara N. Sainath, Research Scientist, Speech Team and Yonghui Wu, Research Scientist, Google Brain Team

Traditional automatic speech recognition (ASR) systems, used for a variety of voice search applications at Google, are comprised of an acoustic model (AM), a pronunciation model (PM) and a language model (LM), all of which are independently trained, and often manually designed, on different datasets [1]. AMs take acoustic features and predict a set of subword units, typically context-dependent or context-independent phonemes. Next, a hand-designed lexicon (the PM) maps a sequence of phonemes produced by the acoustic model to words. Finally, the LM assigns probabilities to word sequences. Training independent components creates added complexities and is suboptimal compared to training all components jointly. Over the last several years, there has been a growing popularity in developing end-to-end systems, which attempt to learn these separate components jointly as a single system. While these end-to-end models have shown promising results in the literature [2, 3], it is not yet clear if such approaches can improve on current state-of-the-art conventional systems.

Today we are excited to share “State-of-the-art Speech Recognition With Sequence-to-Sequence Models [4],” which describes a new end-to-end model that surpasses the performance of a conventional production system [1]. We show that our end-to-end system achieves a word error rate (WER) of 5.6%, which corresponds to a 16% relative improvement over a strong conventional system which achieves a 6.7% WER. Additionally, the end-to-end model used to output the initial word hypothesis, before any hypothesis rescoring, is 18 times smaller than the conventional model, as it contains no separate LM and PM.

Our system builds on the Listen-Attend-Spell (LAS) end-to-end architecture, first presented in [2]. The LAS architecture consists of 3 components. The listener encoder component, which is similar to a standard AM, takes the a time-frequency representation of the input speech signal, x, and uses a set of neural network layers to map the input to a higher-level feature representation, h^enc. The output of the encoder is passed to an attender, which uses h^enc to learn an alignment between input features x and predicted subword units {y_n, … y₀}, where each subword is typically a grapheme or wordpiece. Finally, the output of the attention module is passed to the speller (i.e., decoder), similar to an LM, that produces a probability distribution over a set of hypothesized words.

Components of the LAS End-to-End Model.

All components of the LAS model are trained jointly as a single end-to-end neural network, instead of as separate modules like conventional systems, making it much simpler.
Additionally, because the LAS model is fully neural, there is no need for external, manually designed components such as finite state transducers, a lexicon, or text normalization modules. Finally, unlike conventional models, training end-to-end models does not require bootstrapping from decision trees or time alignments generated from a separate system, and can be trained given pairs of text transcripts and the corresponding acoustics.

In [4], we introduce a variety of novel structural improvements, including improving the attention vectors passed to the decoder and training with longer subword units (i.e., wordpieces). In addition, we also introduce numerous optimization improvements for training, including the use of minimum word error rate training [5]. These structural and optimization improvements are what accounts for obtaining the 16% relative improvement over the conventional model.

Another exciting potential application for this research is multi-dialect and multi-lingual systems, where the simplicity of optimizing a single neural network makes such a model very attractive. Here data for all dialects/languages can be combined to train one network, without the need for a separate AM, PM and LM for each dialect/language. We find that these models work well on 7 english dialects [6] and 8 Indian languages [7], while outperforming a model trained separately on each individual language/dialect.

While we are excited by our results, our work is not done. Currently, these models cannot process speech in real time [8, 9, 10], which is a strong requirement for latency-sensitive applications such as voice search. In addition, these models still compare negatively to production when evaluated on live production data. Furthermore, our end-to-end model is learned on 22,000 audio-text pair utterances compared to a conventional system that is typically trained on significantly larger corpora. In addition, our proposed model is not able to learn proper spellings for rarely used words such as proper nouns, which is normally performed with a hand-designed PM. Our ongoing efforts are focused now on addressing these challenges.

Acknowledgements
This work was done as a strong collaborative effort between Google Brain and Speech teams. Contributors include Tara Sainath, Rohit Prabhavalkar, Bo Li, Kanishka Rao, Shankar Kumar, Shubham Toshniwal, Michiel Bacchiani and Johan Schalkwyk from the Speech team; as well as Yonghui Wu, Patrick Nguyen, Zhifeng Chen, Chung-cheng Chiu, Anjuli Kannan, Ron Weiss and Navdeep Jaitly from the Google Brain team. The work is described in more detail in papers [4-11]

References
[1] G. Pundak and T. N. Sainath, “Lower Frame Rate Neural Network Acoustic Models," in Proc. Interspeech, 2016.

[2] W. Chan, N. Jaitly, Q. V. Le, and O. Vinyals, “Listen, attend and spell,” CoRR, vol. abs/1508.01211, 2015

[3] R. Prabhavalkar, K. Rao, T. N. Sainath, B. Li, L. Johnson, and N. Jaitly, “A Comparison of Sequence-to-sequence Models for Speech Recognition,” in Proc. Interspeech, 2017.

[4] C.C. Chiu, T.N. Sainath, Y. Wu, R. Prabhavalkar, P. Nguyen, Z. Chen, A. Kannan, R.J. Weiss, K. Rao, K. Gonina, N. Jaitly, B. Li, J. Chorowski and M. Bacchiani, “State-of-the-art Speech Recognition With Sequence-to-Sequence Models,” submitted to ICASSP 2018.

[5] R. Prabhavalkar, T.N. Sainath, Y. Wu, P. Nguyen, Z. Chen, C.C. Chiu and A. Kannan, “Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models,” submitted to ICASSP 2018.

[6] B. Li, T.N. Sainath, K. Sim, M. Bacchiani, E. Weinstein, P. Nguyen, Z. Chen, Y. Wu and K. Rao, “Multi-Dialect Speech Recognition With a Single Sequence-to-Sequence Model” submitted to ICASSP 2018.

[7] S. Toshniwal, T.N. Sainath, R.J. Weiss, B. Li, P. Moreno, E. Weinstein and K. Rao, “End-to-End Multilingual Speech Recognition using Encoder-Decoder Models”, submitted to ICASSP 2018.

[8] T.N. Sainath, C.C. Chiu, R. Prabhavalkar, A. Kannan, Y. Wu, P. Nguyen and Z. Chen, “Improving the Performance of Online Neural Transducer Models”, submitted to ICASSP 2018.

[9] D. Lawson*, C.C. Chiu*, G. Tucker*, C. Raffel, K. Swersky, N. Jaitly. “Learning Hard Alignments with Variational Inference”, submitted to ICASSP 2018.

[10] T.N. Sainath, R. Prabhavalkar, S. Kumar, S. Lee, A. Kannan, D. Rybach, V. Schogol, P. Nguyen, B. Li, Y. Wu, Z. Chen and C.C. Chiu, “No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models,” submitted to ICASSP 2018.

[11] A. Kannan, Y. Wu, P. Nguyen, T.N. Sainath, Z. Chen and R. Prabhavalkar. “An Analysis of Incorporating an External Language Model into a Sequence-to-Sequence Model,” submitted to ICASSP 2018.

Facebook pushes pre-roll ads on Watch as it stops subsidizing Live

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A new version of Mixer, Microsoft’s Twitch rival, hits iOS and Android

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Facebook pushes pre-roll ads on Watch as it stops subsidizing Live

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How to Dictate Email in Microsoft Outlook

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10 Environmental Games That Teach Kids About Earth, Ecology & Conservation

The kids of today will inherit the Earth of tomorrow. They will also be left to clean up the mess we leave behind today. With the right ecological education, we can hope they will hit the ground running. A lot of schools and educational institutions are doing their bit by including the environment as part of the curriculum. Words like “carbon footprint” and “global warming” come to them as easily as the name of any present-day rock star. Technology has a trick. Environmental education can be taken out of musty textbooks and turned into interactive games. Like any other strategy game, kids...

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Snapchat launches augmented reality developer platform Lens Studio

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Snapchat launches augmented reality developer platform Lens Studio

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A week on the wrist with the Alpina Startimer

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9 Safari Settings You Should Change for a Better Browsing Experience

A lot of Mac enthusiasts prefer using Safari over Chrome, thanks to its low battery consumption. With macOS High Sierra, Safari is better than ever before, but as with all browsers there are quirks that need fixing. Fortunately enough, most of the browser’s annoying quirks can be fixed by making a quick visit to Safari’s settings. Others need a little more work, but we’ll cover those step by step. Ready to upgrade your Safari browsing experience on your Mac? Let’s begin with fixing Safari’s default preferences first. 1. Enable Link Preview in the Status Bar When browsing through websites, you come across all sorts of hyperlinks. Some links...

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Trim, Cut, or Split a Video the Quickest Way for Free

With phones, DSLRs, and GoPros we’re all shooting more video than ever. But it rarely comes straight out of the camera in perfect shape. Often you’ll need to do a little editing of a video before showing or sharing it. You might want to trim a few seconds off the start or end, or cut it so that it’s a more shareable length. Fortunately, you don’t need any editing skills to do this. You just need the right software — and you’ve probably already got it installed on your computer. So let’s take a look at the quickest way to trim videos...

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Still Using Internet Explorer? 9 Questions Answered

Internet Explorer isn’t the most popular browser, and Microsoft has stopped adding new features to it. Yet it’s still used by many today. Whether you’re forced to use it for work or just love Internet Explorer for personal reasons, you should know how to use it effectively. Here, we’ve gathered some frequently asked questions about Internet Explorer (IE). Read on to find easy answers for some of the most important functions of the browser. 1. What Is the Latest Version of Internet Explorer? The newest (and last) version of Internet Explorer is version 11. Only Windows 7, 8.1, and 10...

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How to Use Vineyard to Run Windows Apps on Linux

Linux is an awesome operating system landscape with a smattering of distributions for all purposes. There are tons of motivations for switching, from monetary savings to learning new skills, and simply supporting the open-source community. However, when switching from Windows to Linux, certain programs cease to properly function. Wine is an application compatibility layer which allows users to run Windows apps on Linux or Mac. Using Wine frontends such as PlayOnLinux or Vineyard significantly simplifies the installing and running Windows apps on Linux with Wine. Learn all about Vineyard, including what it is and how to install it. What Is...

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Azulle Byte 3 Review: This Tiny, Fanless Mini PC Does Everything

Our verdict of the Azulle Byte 3 Mini PC:The Byte 3 is a perfect media center and general computing device for most people's needs, with an attractive design and running the full Windows 10. However, the included remote could have been much better, and the raw performance is lacklustre. With US-based support, we think the $200 price point is just about right. 810.From US-based Azulle Tech comes the latest in a line of fanless mini PCs: the Byte 3. Retailing at around $200 for the base model, that includes a full edition of Windows 10 Pro. Is it good enough for...

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Twitter Makes It Easy to Create a Thread of Tweets

Twitter has long been hamstrung by the limited number of characters afforded to its users. The 140 character limit was born out of necessity, but became both a blessing and a curse. Twitter recently increased its character limit to 280, and it’s now making it easier to thread tweets. Threads have always been possible on Twitter, with people replying to themselves with numbered tweets. These have come to be known as tweetstorms because they usually read as angry rants on a subject close to the person’s heart. And now Twitter is making tweetstorms official. Creating a Tweetstorm Is Now Easy...

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