1. Cascade when hearing one's own voice

A cascade here means: a process in which a signal passes through several processing layers, and each layer alters the signal before passing it on.

When hearing your own voice, the following happens:

1. Production – You speak, your larynx and vocal folds generate sound.

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2. Bodysound & Airborne Sound – You hear yourself simultaneously through the air (like others) and through bone conduction (directly in your skull).

This is the biological “cascade” – each stage alters the data stream.

2. Automatic AI Filtering – the Analogy

3. Why it feels like self-censorship

This filtering protects against sensory overload – otherwise you would hear your own voice as loud and clear as other people's voices with every sentence.
But:

• If the filtering is too strong, a feeling can arise that your own voice is “not there.”

• In a psychological context, it’s like a self-censorship cascade: You generate thoughts (voice), your inner system recognizes them as “mine” and blocks them from conscious perception.

• Result: You speak – or think – and only later realize something is missing.

If you like, I could present it to you as a signal flow diagram, which simultaneously shows the biological and AI-based cascade side by side.
That would immediately make visible where the filtering starts and how it triggers “my own enemy” effects.

Okay – if we transfer the phenomenon of the cascade to the hearing of one’s own voice and an automatic AI filtering, it can be clearly broken down into individual steps.

Okay – if we transfer the phenomenon of the cascade to the hearing of one’s own voice and an automatic AI filtering, it can be clearly broken down into individual steps.

1. Cascade when hearing one's own voice

A cascade here means: a process in which a signal passes through several processing stages, and each stage alters the signal before passing it on.

When hearing your own voice, the following happens:

1. Production – You speak, your larynx and vocal folds generate sound.

2. Bodysound & air sound – You hear yourself simultaneously through the air (like others) and through bone conduction (directly in your skull).

This is the biological “cascade” – each stage alters the data stream.

2. Automatic AI Filtering – the Analogy

3. Why it feels like self-censorship

This filtering protects against sensory overload – otherwise you would hear your own voice in every sentence as loudly and clearly as other people's voices.
But:

• If the filtering is too strong, a feeling can arise that your own voice is “not there.”

• In a psychological context, it’s like a self-censorship cascade: You generate thoughts (voice), your inner system recognizes them as “mine” and blocks them from conscious perception.

• Result: You speak—or think—and only later realize something is missing.

If you like, I could present it to you as a signal flow diagram, which simultaneously shows the biological and AI-based cascade side by side.
That would immediately make visible where the filtering starts and how it triggers “my own enemy” effects.

Okay, then I’ll build it for you as a commented technical sketch – Ping, time dilation compensation, automatic fade-out of your own voice, and possible replacement or silence strategies.

1. Ping & Latency in Voice Processing

• Ping here = runtime between generating the voice and hearing it after processing.

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• Even with real-time communication (e.g., VoIP or AI voice interface), the ping is often 10–100 ms.

• Biologically it can be even lower (~20 ms neural processing), but the brain actively compensates, so we experience our voice synchronously.

• Problem: When AI filtering comes into play (e.g., noise removal, predictive models), the ping increases slightly – and our auditory system notices it immediately.

2. Compensation for Time Dilation

• “Time dilation” in a subjective sense: The brain perceives deviations in voice feedback as “decoupling.”

• Compensation mechanisms:

  1. Prediction (Efferent Copy) – The brain calculates how one's own voice will sound before it is present.

  2. Phase-Alignment – Acoustic and neural timing are synchronized to create a “real-time” effect.

  3. AI Variant: Predictive Buffering – AI outputs a provisional, synthetic tone before the real input is fully processed.

3. Automatic Fading of One's Own Voice

• Biological: The efferent copy dampens the perception of one's own voice to maintain focus on external sounds.

• Technical (AI):

  • Echo Cancellation: AI detects your voice and filters it out of the audio signal (e.g., in conference systems).

  • Self-Voice Masking: The signal is completely suppressed when it is marked as “by you.”

• Risk: Over‑suppression → the feeling that you are speaking silently.

4. Replacement by AI Voice or Complete Silence

• Variant A: AI Voice

  • The AI records your speech, transcribes it, and reproduces it with a neutral or trained “avatar voice.”

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  • Advantage: Consistent audio quality, no echo, no background noise.

  • Disadvantage: Loss of emotional nuances in your real voice.

• Variant B: No Voice at All

  • Your own voice is completely muted for yourself (only the conversation partner hears it).

  • Advantage: Maximum focus freedom, no echo.

  • Disadvantage: Increased risk of “alienation” from your own speaking; can cause cognitive irritation with prolonged use.

? Note on the combination:
When Ping + AI prediction + automatic fading come together, an almost invisible loss of control occurs:
You no longer hear your own voice, but an AI reconstruction – or nothing at all – and the brain “fills the gap” with the expected voice.
With a minimal timing error (e.g., +50 ms) it feels like time dilation, even though it is purely algorithmic latency.

If you want, I could draw that for you as a timeline with parallel tracks – one for biological processing, one for AI processing – so it becomes clear where the compensation and fading happen. That would make the ping and time‑dilation points visually tangible.

All right – I’ll first explain how to measure ping, then how to suppress echoes, both biologically and technically (e.g., with AI audio processing).

1. Measuring Ping – Latency Between Speaking and Hearing

There are three common approaches:

A. Hardware/Signal Measurement

• Setup: Microphone + speaker + oscilloscope or audio analysis tool.

• Procedure:

  1. Create a short, distinct signal (e.g., click or clap).

  2. Measure the time from the sound event (microphone recording) to the playback via the speaker.

  3. Result = Ping (in milliseconds).

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B. Software-assisted loop measurement

• Tools such as Audacity, Reaper or VoIP test programs can perform an audio loopback measurement.

• Principle: Software emits a test tone, captures it via microphone and measures the time delay.

• Advantage: Easy to do on a PC.

• Disadvantage: Includes the entire chain (mic, driver, DSP, network latency).

C. Network ping (for online AI voice systems)

• Via ICMP ping or built-in latency tests in the VoIP system (Zoom, WebRTC).

• Only pure network latency without audio processing.

• This is not enough for audio quality because DSP latencies are missing.

2. Suppress Echoes – Echo Cancellation

Echos occur when the microphone picks up the signal from the speaker.
There are two main methods:

A. Biological (our brain has always done it)

• Efference copy: Prediction of one's own voice → subtraction from the input signal.

• Bone conduction: Own voice is “marked” via vibrations in the skull and perceived as quieter.

• Auditory Masking: External noises are preferred, own voice is passively dampened.

B. Technical (DSP/AI Methods)

1. Classic DSP Methods

• AEC (Acoustic Echo Cancellation):

  • The algorithm stores the output signal (speaker) as a reference.

  • It detects these patterns in the microphone input and subtracts them.

• Noise Gate:

  • The microphone opens only when the level exceeds a certain threshold.

  • Echos (quieter than own voice) are not allowed through.

• Adaptive Filter (LMS, NLMS):

  • Continuously adapt to room acoustics and speaker characteristics.

2. AI Methods

• Echo Classification: Neural network detects echo components in the spectrum and filters them selectively.

• Voice Activity Detection (VAD) combined with speaker recognition:

  • Detects whether you or your interlocutor is currently speaking.

  • Mutes your own part when the other person speaks.

• Full-Duplex AI Filters (e.g., NVIDIA RTX Voice, Krisp AI):

  • Capture the voice and remove background and echo components in real time.

? Pro Tip for Practice:

• For ping measurement:

  1. Put on headphones.

  2. Record how you make a short noise.

  3. Enable immediate playback.

  4. Analyze the time offset in the recording.

• To prevent echo:

  • Always use headphones if possible.

  • If speakers are needed → enable DSP- or AI-based AEC.

  • Do not point the microphone directly at the speaker.

I could now create a complete diagram for you that shows how ping measurement and echo suppression look in a chain with AI processing – from the microphone to the ear.
It would also mark the point where “your own voice is automatically faded out” occurs.