There is a simple test: play a recording that seems unclear, then look at the transcript. If it suddenly sounds clear the moment you can read along, the problem was never the audio.
Poor transcription because you don't know words or you don't know relationships between words.
That is the more precise account. The audio is not unclear — the words are unfamiliar. A vocabulary problem presents as an audio quality problem.
"Mi-au spus" versus "m-au spus": why is it that I hear it rightly when I see it?!
Seeing the word unlocks the phoneme — the signal was there all along. This also shows what kind of task transcription actually is. Checking an answer when the transcript is visible is easy; generating it from sound alone is hard. They are not the same problem. Dictation forces the harder direction. The ear only becomes capable of generation once it has attuned to the corpus, and that attunement is what the practice builds.
The native speakers does not speak clearly to your ear.
Not yet. The same recordings that seem unclear to a new listener are understood without effort by a native speaker — not because of better ears, but because they know the words, and because the trained ear anticipates. It resolves not just forward — predicting words that haven't arrived yet — but backward too: a syllable half-caught, plus context from what follows, and the gap fills in. This runs in both directions simultaneously. It is not a skill you learn consciously. It accumulates from exposure.
The corollary is worth noting. With high-quality audio — a podcast through headphones, crisp consonants delivered cleanly — comprehension is easier precisely because less of this work is required. The cleaner the signal, the less the brain needs to anticipate and resolve. That may be comfortable. It may also retard the development of second-nature comprehension of natural, imperfect speech — the kind you will encounter everywhere outside the headphones.
For this reason, I no longer do dictation exercises with headphones. The audio clarity is too far from the actual target situation: offline, engaged with a native speaker at a normal social distance, with ambient noise — a restaurant terrace, a TV in the background, a street. A headset removes all of that and delivers a signal that nothing in real life will match. Training on it is training on the wrong thing.
The counterargument is that podcast-quality input provides more comprehensible input from the start — and for a beginner, that may be true. More CI early means more material the brain can actually process, which builds the substrate faster. That argument has some merit. But comprehensible input optimized for early stages may not be what takes you past B1. The actual target is understanding speech in public, in motion, against ambient noise, from speakers who are not editing for your benefit. That requires adaptation to limited, imperfect reception — and that adaptation only comes from practicing under those conditions. Imperfect audio with spaced repetition trains exactly that. The SRS handles what the single exposure cannot: it returns the clip until the reception improves.
Machines doing ML are not bothered by audio quality, but human ears are.
At first. At sufficient volume of exposure, the ear learns what it formerly could not decode. The tolerance is an output of the process, not a condition for starting it.
A recording that is hard today will come back. The SRS is not asking you to master every clip on first contact — it is asking you to attempt it and let the schedule return it when there is more to bring to it. The resistance levels — around one month in, around six months — are where it feels like nothing is working and most people quit. What is actually happening at those points is that the substrate is building without yet being visible as output.
When a clip seems impenetrable, attempt it anyway. Write whatever registered — a word, a fragment, a syllable. The commitment is the mechanism. The correctness is not.
A quality-classified corpus — cleaner clips weighted toward early sessions, natural-noise clips as the substrate develops — would be a genuine improvement and is something we want to build. It does not change the destination; it changes the onramp.