20 Tech Tips from Joe Dale

Joe Dale is a wealth of ed-tech tips and information

For anyone unfamiliar with Joe Dale, I highly recommend you check out his YouTube channel and follow him on Twitter. The man is an absolute wealth of tech tips for language teachers. Here are a few gems I picked up from him in a single Zoom session:

  1. Make any video your lesson with EdPuzzle
  2. Visualize your ideas in a new and collaborative way using JamBoard
  3. Easily add transcribed voice comments and feedback to shared documents using the Mote Google Chrome extension
  4. Allow students to create digital learning portfolios with Seesaw
  5. Quickly and easily record your voice with Vocaroo or OnlineVoiceRecorder
  6. Immersive Reader, included in OneNote Learning Tools, is a full screen reading experience to increase readability of content in OneNote documents
  7. Ferrite Recording Studio makes it fast and easy to record and edit audio, and includes powerful features such as effects and automation
  8. Voice Record Pro 7 is a professional voice recorder for iOS
  9. Textivate generates a wide range of interactive activities based on your own text and / or matching items. It works with texts of up to 500 words and / or up to 200 matching items
  10. Teach any language with YouTube + TeachVid
  11. LearningApps.org is a Web 2.0 application, to support learning and teaching processes with small interactive modules
  12. You can easily allow anyone to create a copy of a Google doc you have created by changing the end of the URL from /edit?usp=sharing to copy: https://docs.google.com/document/d/1lQdVTkuiT6oi-CZ9A9y6rrCXOyoX8VeSgBw-sH94WHA/edit?usp=sharing -> https://docs.google.com/document/d/1lQdVTkuiT6oi-CZ9A9y6rrCXOyoX8VeSgBw-sH94WHA/copy
  13. Easily create any kind of Google Drive doc with the following URL shortcuts: doc.new, form.new, slides.new
  14. Use Ilini to learn French with the best videos on the web
  15. Create presentations, infographics, and more with Genially
  16. Create your own personal Emoji with Bitmoji
  17. Get popup translations for any website using Lingro
  18. Get easy-to-understand multilingual definitions with WordReference.com
  19. Exam.net is a robust, easy-to-use and secure exam platform
  20. Draftback is a Chrome extension that lets you play back any Google Doc’s revision history

How does Speech Recognition work, and how can it help us teach English? (Part 1)

Automatic Speech Recognition (ASR) seems to be everywhere these days, from your smart fridge, to your smart phone, and every device in between. But how does it actually work, and how can it be utilized by teachers of English?

In the first part of this blog post, we learn how speech is transformed from vibrations in the air to text on your screen. In the second part (coming soon!), we take a look at some of the ways speech recognition can be used as a teaching and testing tool in English language pedagogy.

Step 1. Analog to digital

Humans live in an analog world. When we speak to each other, we don’t transmit streams of numbers to each other; we vibrate our vocal chords, which create sound waves that vibrate other people’s eardrums, which send electrical signals into the brain, which the brain interprets as words. Unfortunately, computers can’t process sound waves without first converting them into a digital form, i.e. a stream of numbers. 

This is exactly what a microphone does. A microphone is basically an analog-to-digital converter (ADC), which changes vibrations in the air into electrical signals that can be represented by numbers. However, this is all a microphone can do. It can convert an analog audio wave into a digital stream of numbers, but it has no idea what words (or other sounds) those numbers represent.

In order to recognize words, we need a computer program that can break the recorded sound down into its individual phonemes, and then connect those phonemes into the most likely combinations to form words.

Step 2. Identifying phonemes

A phoneme is the smallest significant part of a spoken word. The word “cat”, for example, consists of three phonemes, transcribed in ARPABET as: 


What rule can we specify to allow our computer to determine whether a certain segment of a sound recording is the phoneme “AE” in “cat”? It is not an exact science. Different speakers pronounce the “AE” phoneme differently depending on their accent, their tone of voice, their vocal timbre, their age, gender, and even emotional state.

Instead of trying to come up with a rule for what the “AE” phoneme sounds like, we can feed a Machine Learning (ML) algorithm thousands of hours of English speech, and allow it to figure out for itself what the “AE” phoneme is supposed to sound like. Then we can ask the algorithm:

Given that these sounds are all “AE”, is this sound also “AE”?

An important point to note here is that the algorithm is not trying to figure out which phonemes individual words are made up of. This process has already been completed by language experts, who have released dictionaries of word-phoneme mappings that can be used to train speech recognition engines.

What the ML algorithm is trying to do is map sounds to phonemes, and then connect those phonemes into the most likely combinations to form words.

It does this by chopping up phonetically annotated sound clips into very short (25ms) frames. Each frame is converted to a set of numbers which represent the different sound frequencies in the frame. The ML algorithm then learns to associate certain frames or combinations of frames with the corresponding parts of the phonetic transcription.

Every time the training program encounters the “AE” phoneme, it accommodates the new example in its Acoustic Model (AM) of the sound, thereby building up a comprehensive representation of what the “AE” phoneme should sound like.

Step 3. Connecting phonemes

Once the algorithm has processed all of the training data, we can then ask it to identify an audio recording of the word “cat”. It will break the recording down and analyze it, as described above, it an attempt to identify its constituent phonemes.

However, because some phonemes (and consequently some words) have incredibly similar pronunciations, sometimes the computer’s best guess at the recording’s constituent phonemes isn’t accurate enough for reliable speech recognition. Fortunately, there is a way to improve the computer’s accuracy.

We can narrow down the possible phoneme choices by employing a statistical algorithm called Hidden Markov Model (HMM). HMM uses statistical probability to determine the likelihood of a future state (the next phoneme in the sound) given a current state (the current phoneme in the sound). 

When it comes to phonemes in the English language, certain combinations are much more likely than other combinations. For example, “Z” in “zebra” never follows the phoneme “C” in “cat”, but “AE” in “cat” often follows “C” in “cat”.

When a speech recognizer is attempting to map a sound to its constituent words and phonemes, it will give precedence to likely combinations of words and phonemes over unlikely or impossible combinations. It knows what the likely combinations are by referring to a large database of phonetically transcribed recordings, known as the Language Model (LM).

For example, the sentence “Dolphins swim” is much more likely to occur in the English language than “Doll fins swim”, even though “dolphins” and “doll fins” are comprised of exactly the same sequences of phonemes.

Step 4. Hello computer!

We now have a computer program that can analyze recorded sound and convert it into the most likely sequence of words.

But how does all of this help English learners to improve their speaking skills? Read Part 2 to find out! (Coming soon!)

20 Tech Tips from JALT CALL 2019

The 2019 JALT CALL conference was informative and enjoyable as usual! Here are some handy highlights and tech tips I picked up during the three days of presentations…

  1. The big names that come up every year include English Central, WordEngine, Pocket Passport, and XReading. Check them out if you don’t already know them!
  2. Did you know you can use MoodleCloud to host your Moodle installation?
  3. According to English Central, “difficulties”, “colony”, and “discovered” are amongst the words Japanese learners of English find the most difficult to pronounce
  4. The University of Kyoto is using blockchain to power its learning analytics. Find out more about the uses of blockchain here
  5. Kai-Fu Lee discusses AI in his best-selling book “AI Super Powers
  6. Musio X robot helps Japanese kids learn English
  7. Google Duplex can call local businesses to arrange appointments
  8. Pocket Talk puts the power of two-way voice translation in your pocket
  9. Translatotron can translate L1 speech directly into L2 speech without the need for an intermediary text transcription stage
  10. Critical thinking, people management, and creativity will be among the top 10 job skills in 2020 according to the World Economic Forum
  11. DialogFlow can be used to create natural AI-powered “conversation experiences”
  12. Seesaw empowers students to demonstrate and share learning
  13. Google Classroom is gaining traction in Japan, although I experienced issues inviting students from certain institutions that hadn’t yet granted access to the tool
  14. Did you know that Google complies with the EU’s General Data Protection Regulations (GDPR)?
  15. Did you know that there are 118 million smart speakers in US households?
  16. Alexa Skill Blueprints allow you to easily create your own Alexa Skill
  17. jb11.org contains lots of useful text analysis tools
  18. Learner English corpora include ICLE, JEFFL, and many others
  19. There are also many native speaker corpora
  20. Manaba is a popular LMS in Japan

Also, don’t forget to check out my own sites:

… and buy my book if you’re interested in learning more about how to use tech in the ESL classroom!

My appearance on ALTInsider.com

I was delighted to appear on the excellent and informative ALTInsider.com podcast with James last weekend. The episode has just been released, and I talk about Computer Assisted Language Learning, writing graded readers, and teaching at universities in Japan.

Here is a quick rundown of my sites mentioned on the podcast:

Also, don’t forget to check out my book of tech tips for English teachers, and support me on Patreon if you find my work useful.

25 Tech Tips from JALT 2018


  1. Did you know that Google Slides now offers a feature to display automatic closed captions (i.e. it types what you say as you speak!)
  2. John Blake offers a variety of web-based tools for language teachers and learners
  3. Regex101 helps you write regular expressions
  4. Google Sites can be used to store handouts, host video and audio, link to useful websites, and a range of other useful functions
  5. Did you know that it’s possible to slow down and speed up YouTube videos?
  6. Google has a vocabulary learning activity built in to its mobile search portal
  7. Ozdic is a useful collocation dictionary
  8. YouGlish allows students to use YouTube to improve their English pronunciation
  9. The Google Public Data Explorer makes large datasets easy to explore, visualize and communicate
  10. Gapminder produces free teaching resources making the world understandable based on reliable statistics
  11. ToPhonetics allows you to convert English text to IPA phonetic transcription
  12. Clip2Comic (iOS app) is a useful app for converting photos to comics for storytelling and other educational purposes
  13. CNN now produces reading and listening lessons for English learners, including text-to-speech audio and a vocabulary look-up feature…
  14. … while DreamReader provides free English reading practice for learners
  15. PowToon allows you to create engaging animated videos with a library of styles, characters, backgrounds
  16. Unsplash provides free stock photography for any purpose…
  17. …as does Pexels
  18. Cambridge World of Better Learning provides insights, tips and tools for language teachers
  19. Pocket Passport provides flashcards, storyboards, digital quizzes, and other resources for English language teachers
  20. EnglishCentral offers a series of high frequency vocabulary lists to help identify gaps in students’ knowledge, in addition to an online vocabulary level check
  21. Just-in-time learning involves using technology to consume learning materials at any time and in any place
  22. Did you know that MEXT officially promotes the use of ICT for active learning and for increasing the amount of time spent engaging with foreign languages?
  23. Scott Sustenance has developed an innovative system based on “mnemotechnics” (a.k.a. the “keyword method”) for enhancing students’ vocabulary recall ability. Check out his students’ work on his Instagram feed: #kwvocab18
  24. Nearpod provides a variety of real-time activities suitable for language classrooms, including open ended questions, fill-in-the-blanks, matching activities, and more
  25. The Font is an online journal of quality writing on the theme of teaching and learning languages at home and abroad

If you found these tips useful, why not check out the new version of my book, which has been revised, updated and expanded for 2019: 50 Ways to Teach with Technology