DialogCheck Review

I don’t think I’d be going too far out on a limb to say that most people who enjoy streaming media at home have been involved in at least one discussion about the intelligibility (or lack thereof) of dialogue, particularly when watching theatrical releases on the small screen. At first we might have found ourselves asking, “Is it just me?” Then later we realized that many of our friends were consistently watching movies with subtitles on.

There have been newspaper articles, blog posts, YouTube videos, and social media posts on the subject. And these critiques have not fallen on unheeding ears. NUGEN Audio, makers of the LUFS & Loudness Meter Plugin VisLM, have just released DialogCheck, a plugin that promises to do for dialogue intelligibility what VisLM does for loudness compliance.

AI Powered

In order to set a standard for intelligibility, you first need to establish a consistent standard of measurement. Intelligibility can be subjective, and once an audio engineer has heard (or read) the dialogue, perhaps hundreds of times, it’s difficult for them to put themselves into the shoes of a person viewing the film for the first time. The developers at NUGEN Audio have devised a solution using dual AI neural networks to analyze audio data and create a consistent, objective scale on which to grade speech clarity.

In spite of the complex analysis going on behind the scenes, DialogCheck is extremely easy to use. Insert it into your Master, select a preset, and hit play (you can also analyze your audio with an offline render if you don’t feel like listening in real time). DialogCheck was developed in consultation with Netflix, so it comes with a Netflix preset right out of the box.

A Comprehensive Interface

If you’ve used VisLM, the user interface style will be familiar. DialogCheck gives you a combination of graphic and numeric representations of your data, organized in an instantly digestible format. The interface colours are user-selectable, but I’ll use the Netflix preset as an example, as the traffic-light colour scheme is pretty intuitive.

Arguably, the most significant number is the dividing line between medium and high (yellow and green, respectively), which in the case of the Netflix preset is 60. Higher numbers indicate greater clarity. Once your audio is analyzed, you will have a scrolling graph that tells you at a glance how intelligible your dialogue is – green is good, yellow is marginal, red is bad. Areas with no colour indicate audio in which dialogue was not detected.

On the right side of the window are Realtime and Statistical numerical readouts. Momentary Clarity is an instantaneous indicator of the current intelligibility, while Upper Quartile, Median, and Lower Quartile are calculated based on the entire stored analysis. Upper Quartile is the average clarity of the best dialogue, and Lower Quartile represents the not-so-good dialogue. On the far left is the Distribution view, which gives you an indication of how your dialogue is doing overall – the more time it’s spending in the green, above your threshold, the better. Scanning through your stored analysis is accomplished by clicking and dragging the bottom timeline left and right – dragging up and down adjusts the time scale, so you can find problem areas in longer program material at a glance.

Conclusions

DialogCheck was created for audio professionals working in film and video fields, but can be applied to any media where dialogue intelligibility is important, including radio, podcasting and video game audio. It supports surround sound up to 7.1.4 and is available in AAX, VST3 and AU formats. I think dialogue intelligibility guidelines are something industry professionals and consumers alike can get behind (with the possible exception of Christopher Nolan), and given DialogCheck’s ease of use – a novice like me can set it up successfully without cracking the manual – I expect it to be a common sight on screens in post-production studios before long.

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