Workshop Perceptual Effects of Filtering and Masking Introduction to Filtering and Masking

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1 Workshop Perceptual Effects of Filtering and Masking Introduction to Filtering and Masking The perception and correct identification of speech sounds as phonemes depends on the listener extracting various relevant acoustic cues from the speech signal. Some of these cues are much more important than the others (eg. lower formants are more important than higher formants in vowels). There is usually a degree of redundancy in the signal. That is, there may be more cues available than are needed to identify the phoneme. 1. Filtering Acoustic filtering is the selective removal of some part of a sound and the retention of another part of the sound. If some of the perceptual cues are removed from a speech signal (ie. a speech sound) by filtering, the identification of the sound as a phoneme becomes more difficult. The more important the cue removed, the more likely that the sound will be mis-identified. Sometimes the removal of a cue will leave a sound with only enough information to allow identification of the manner of articulation, but not the place of articulation (or vice versa) or will permit the identification of a front vowel as a front vowel but will not allow the identification of its height and thus its precise identity, etc... This implies that certain acoustic cues are highly correlated with certain phonetic features or categories. (Miller and Nicely, 1954) For example, you should be aware that vowel F1 is strongly correlated with vowel height and that the removal of F1 will remove the main cue for vowel height. This will cause vowels with approximately the same F2 (correlated with vowel fronting) to be confused with each other. That is, front vowels will be confused with other front vowels and back vowels will be confused with other back vowels, etc. (Agrawal and Wen (1975)) Acoustic filters are of four basic kinds:- a. Low-pass (LP) filters b. High-pass (HP) filters c. Band-pass (BP) filters d. Band-stop (BS) filters a) Low-pass (LP) filters Low pass filters selectively retain (ie. allow to "pass" through) all frequency components of a sound below a certain frequency (the cut-off frequency) and attenuate (ie. remove) all frequency components above that frequency. In figure 1 the thick black rectangle represents an idealised LP filter. An idealised LP filter has an extremely sharp cutoff (in this example at 1400 Hz) and frequency components of a speech signal are passed without any alteration below this frequency and are completely removed above this frequency. The thin red line represents a more realistic LP filter cutoff for a 1400 Hz LP filter. In most practical LP filters the cutoff slope is more gentle with some attenuation below the cutoff frequency, a 3 db attenuation at the cutoff frequency, and a gradual increase in attenuation (ie. decrease in amount of signal retained) above the cutoff frequency.

2 Also shown in figure 1 is an idealised vowel spectrum (the blue line) showing the position of its formants, with the three most important formants labelled. When this vowel is passed through the filter only the first formant is permitted to pass. All other formants are removed. The green shaded area approximately represents the spectrum that will remain after filtering with this filter. Since F1 correlates (inversely) with vowel height, the listener will be able to determine the height of this vowel. Since F2 correlates with vowel fronting the listener will not be able to determine whether this is a front, central or back vowel (and will have to guess which it is). You should note that not all vowels have F2 values greater than 1400 Hz and so not all vowels will have their fronting cue removed by this filter. This means that the fronting characteristics of some vowels will be identified accurately and some will not. For adult male speakers F1 is always less than 1400 Hz. Figure 1: Idealised vowel spectrum (blue line) with a superimposed idealised low pass filter (black rectangle) and a more realistic low pass filter (red line) which slopes down more gradually. Only the green shaded part of the spectrum passes through the filter. LP filtering of speech is analogous to what occurs in auditory systems with a high frequency hearing loss. In such auditory systems high frequency components of sounds are attenuated and inaudible whilst at least some of the low frequency components of sounds are passed through to the auditory cortex. More accurate models of high frequency hearing loss can be achieved by altering the LP cutoff frequency and the attenuation slope to better match the auditory characteristics of each individual speaker. Note, however, that this would only be a partial model of an individual's hearing loss as other aspects of the sound can also be affected and these other aspects of hearing loss are not fully modelled by this simple type of filter. b) High-pass (HP) filters

3 High pass filters selectively retain (ie. Allow to "pass" through) all frequency components of a sound above a certain frequency (the cutoff frequency) and attenuate (ie. Remove) all frequency components below that frequency. c) Band-pass (BP) filters Band pass filters selectively retain (ie. Allow to "pass" through) all frequency components of a sound between two frequencies (the HP and LP cutoff frequencies) and attenuate (ie. Remove) all frequency components below the HP cutoff frequency and above the LP cutoff frequency. d) Band-stop (BS) filters Band stop filters selectively attenuate (ie. Remove) all frequency components of a sound between two frequencies (the HP and LP cutoff frequencies) and retain (ie. Allow to "pass" through) all frequency components below the HP cutoff frequency and above the LP cutoff frequency. A band-stop filter is effectively the inverse of a band-pass filter with the same cutoff frequencies. 2. Masking The effects of masking are not so clear cut as masking does not actually remove a cue but obscures it by overlaying it with noise. Noise masking can be of two kinds, additive or multiplicative. Multiplicative masking occurs when each speech sample is multiplied by a sample from a noise sound. The effect of this is to match the noise level with the level of the speech at each point in time so that for quieter segments (eg. /f/) the added noise level is lower than the level of noise added to louder segments (eg. vowels). Additive noise masking occurs when the average speech level for an entire speech token (in our experiment, either an /h_d/ or a CV syllable) is computed and then the level of noise added to the entire token is a fixed multiple of the speech token's average level. This will have the effect of more greatly masking softer phonemes than it will the louder phonemes. In the present experiment we will be using additive noise masking. We will be using two levels of masking for each of two types of noise. It is extremely important when using noise as a masker of speech to be aware of the spectrum of that noise. We will be using two types of noise:- i) White Noise The simplest type of noise has a flat spectrum across the range of frequencies encountered in speech (in our experiment this is the range 0-5 khz). This type of noise is usually referred to as "white noise" (see figure 2). In figure 2 the intensity scale is not specified, but is normally in decibels (db). The frequency scale is in kilohertz (khz or thousands of Hertz). The spectrum of a noise is random (fluctuates up and down randomly) but the average spectrum (indicated by the thick straight line through the middle of the noise) is flat. That means that the spectrum of white noise is at about the same intensity for all frequencies. You will note the steeply sloping line down to 5 khz. This line indicates that the spectrum of the noise is limited to the frequency range 0 to 5000 Hz. True white noise would have equal intensity up to infinite frequency (obviously impossible) and so white noise is always band-limited to a finite frequency range. The frequency range Hz has been chosen because the speech recordings have also been band-limited to this range. As this is an adult male speaker, almost all acoustic cues to phoneme identity are found below 5000 Hz. Figure 3 shows the actual spectrum and waveform for the white noise signal used in this experiment.

4 Figure 2: Idealised White Noise spectrum Figure 3: Actual spectrum of the white noise signal used in this experiment. Note also the noise waveform in the bottom window. Click on the image to hear the noise.

5 ii) Speech-Shaped Noise Another type of noise commonly used in speech perception experiments is often referred to as speech shaped noise (see figure 4) In figure 4 only the average spectrum is indicated by the curved line. The actual random pattern of the noise has not been superimposed over this line as it was for the white noise. This type of noise has a spectrum which approximates the average long term spectrum of the speech of an adult male and has a slope below 100 Hz of +6 db/octave, a flat spectrum between 100 Hz and 320 Hz, and above 320 Hz a slope of -6 db/octave (this means that each time frequency is doubled, ie. increases by an octave, the intensity drops by 6 db). Speech shaped-noise has a similar effect to the masking produced by a number of other speakers speaking at the same time ("multi-speaker babble") and as such it is a more realistic type of noise to use in speech perception tests as it simulates real life situations (eg. a noisy party). Figure 5 shows the actual spectrum and waveform for the speech-shaped noise signal used in this experiment. Figure 4: Speech-shaped Noise spectrum

6 Figure 5: Actual spectrum of the speech-shaped noise signal used in this experiment. Note also the noise waveform in the bottom window. Click on the image to hear the noise. There are many other types of noise (eg. band limited noise, or noise with various non-speech slopes) but they will not concern us in this experiment. What this experiment attempts to demonstrate is that the actual pattern of confusions produced by masking differs depending on the spectrum of the noise used in the masking (Miller (1947)). White noise and speech shaped noise will selectively mask speech cues differently. Comparison of the Spectra and Masking Properties of White and Speech-shaped Noise Figure 6 compares the spectra of white noise and speech-shaped noise of approximately equal overall intensity. It can be seen that speech-shaped noise might have a greater masking effect (than white noise) on acoustic cues between about 300 and 1000 Hz. This is the frequency range where vowel first formant (F1) and some back vowel second formant (F2) cues are found in adult male speech. White noise, on the other hand would be expected to have a greater masking effect on acoustic cues for frequencies above about 1000 Hz. The resonance peak cues for some fricatives and for the aspiration of some stops can be found above 1000 Hz. Important vowel cues (F2 for mid and front vowels and F3 for all vowels) are found above 1000 Hz. Whilst many consonant cures are found above 1000 Hz, numerous consonantal cues are found below 1000 Hz. Figure 7 compares the average long term spectra of the actual noise signals used in this experiment.

7 Figure 6: Comparison of White and Speech-shaped Noise spectra Figure 7: Actual long-term spectra of white noise (blue line) and speech-shaped noise (red line) of the two noise signals used in this experiment. Both noise signals were level normalised to the same intensity which, when output following calibration with a specific tone was at an average level of 70 db spl (ref: 20µPa).

8 iv) Signal-to-noise Ratio (S/N) This experiment will also examine the effects of different masking levels. Noise masking levels are usually expressed as a signal-to-noise ratio (db Signal/Noise (S/N)). When the signal and the noise level are the same (S/N = 1/1 =1) the S/N expressed in db is 0 (20 x log(1/1)). When the speech sound pressure level is twice that of the noise (2/1) then the masking level is +6 db S/N. When the noise sound pressure level is twice that of the speech (1/2) then the masking level is -6 db S/N. Reading You should read the following chapter from Clark and Yallop. 1. Clark, J.E., Yallop, C., and Fletcher, J., An Introduction to Phonetics and Phonology (3rd edition), Blackwell, Oxford, Chapter 8, "Speech perception". References These references have been referred to in the topic notes All three papers are available to registered Macquarie University students via the library's journal finder (http://www.lib.mq.edu.au/journalfinder/). 1. Agrawal A. and Wen C. Lin, "Effects of voiced speech parameters on the intelligibility of PB words", J.Acoust.Soc.Am. 57, , Miller G.A. "The masking of speech", Psychological Bulletin 44(2) , Miller G.A. and Nicely P.E. "An analysis of perceptual confusions among some English stop consonants."

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