Related Experiment Video
Updated: Aug 9, 2026

10:27
The Power of Interstimulus Interval for the Assessment of Temporal Processing in Rodents
Published on: April 19, 2019
Effect of temporal envelope smearing on speech reception
R Drullman1, J M Festen, R Plomp
1Department of Oto-rhino-laryngology, Free University Hospital, Amsterdam, The Netherlands.
The Journal of the Acoustical Society of America
|February 1, 1994
Summary
Smearing the temporal envelope severely impacts speech intelligibility, especially with narrow frequency bands and low cutoff frequencies. Modulation frequencies above 16 Hz offer limited benefit for sentence recognition.
Area of Science:
- Auditory Perception
- Speech Processing
- Psychoacoustics
Background:
- Temporal envelope processing is crucial for speech understanding.
- The impact of temporal envelope smearing on speech reception threshold (SRT) and phoneme identification is not fully understood.
Purpose of the Study:
- To investigate the effect of smearing the temporal envelope on SRT for sentences in noise.
- To examine the impact of temporal envelope smearing on phoneme identification in normal-hearing listeners.
Main Methods:
- Speech was divided into frequency bands (1/4, 1/2, or 1 oct).
- Amplitude envelopes were low-pass filtered at various cutoff frequencies (0-64 Hz).
- Sentence intelligibility and phoneme identification (vowels, consonants) were assessed.
Main Results:
- Narrow bands and low cutoff frequencies (0-2 Hz) severely reduced sentence intelligibility.
- Modulation frequencies above 16 Hz had minimal impact on intelligibility.
- Consonant identification was more affected than vowel identification, with specific confusions noted for vowels and stop consonants.
Conclusions:
- Temporal envelope smearing significantly degrades speech perception, particularly at low modulation frequencies and narrow bandwidths.
- Consonants, especially stop consonants, are more vulnerable to temporal envelope distortions than vowels.
Related Concept Videos
Interference: Path Lengths
Consider two sources of sound, that may or may not be in phase, emitting waves at a single frequency, and consider the frequencies to be the same.
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
Echo
The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case, then the...
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case, then the...
Reconstruction of Signal using Interpolation
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...
Aliasing
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original signal...
Upsampling
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
Bandpass Sampling
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...

