Monitoring reverberation in the preceding context

10^L/20 (4.10)

where the standard reference sound pressure value is used, p0 = 10⁵Pa (20 micro pascals).

Calculated once for the ‘sir-stir’ reference signal with the desired presentation level of L = 48 dB SPL, the corresponding value of l was stored and subsequently used to scale every other 48 dB SPL presentation of the monaural ‘sir-stir’ stimuli to the auditory model.

4.4.3 Monitoring reverberation in the preceding context

Two signal-based approaches to reverberation quantification were proposed in

§ 4.3, and important aspects of their function were described with reference to an unreverberated signal. In the current section, these two techniques are discussed

in turn to examine whether they do indeed capture something useful about the re-verberation content of the signal in the acoustic context immediately prior to the test-word itself.

The mean-to-peak ratio (MPR) metric works on a specific windowed portion of the context (the recently experienced time), and measures the mean and the peak values experienced, thus keeping track of the signal’s dynamic range. This is displayed in Figure 4.12. In Figure 4.12a, the STEP representing AN firing rate is shown in the top panel for a test utterance reverberated at the near distance. The context portion just prior to the test-word, here Z = 100 frames, corresponding to context window of 1 second duration at the model’s output frame rate, is displayed in the second panel. Finally the third panel shows the across-channel sum, defined earlier in Equation 4.4. This reveals a signal with a large dynamic range, strong modulation content, and relatively sharp onsets and offsets.

The far-distance context is similarly shown in Figure 4.12b. The peak value is of the same order of magnitude as in the near condition, with the most significant dif-ference being that the dips in the temporal envelope have largely been filled with reverberant energy (effectively increasing the noise floor). This increase in energy shows as a rise in the signal’s mean value, which thereby causes a corresponding raise in the value estimating the reverberation content of the signal Rmp(cf. Equa-tion 4.5). If an increase in R_mp value were to be mapped in the efferent circuit to a process of stronger attenuation, it is anticipated that this would decrease the simulated AN response to the late-arriving, low-level reflected energy, and thereby uncover some of the dips in the temporal envelope.

A similar demonstration is given in Figure 4.13 for the low-pass mask (LPM) re-verberation estimator. In this case, only the Z = 100 frames of the context window are shown, again for the response in a single high-frequency auditory channel. For the major region of activity in this channel (at roughly 350 ms before the end of the context portion), a comparison of the binary masks in the near distance (Fig-ure 4.13a) and far distance (Fig(Fig-ure 4.13b) reveals that the increased reverberation adds longer ‘tails’ to the window in the far condition. The negative-going part of the smoothed temporal envelope suggests a masked tail-contribution in this channel over approximately 12 frames in the near condition, and over double this duration in the far context condition.

Again, a higher value of Rlp results for the far-reverberated speech context than for the near-reverberated context. Following the same logic as described for the MPR estimator above, if an increased Rlpwere again mapped to an increased at-tenuation value, then the increase in reverberation could be thought of as activating the efferent suppression mechanism and may possibly simulate effects of MOC unmasking.

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(b) far distance context: MPR measure, Rmp= 0.47

Figure 4.12: Mean-to-peak ratio (MPR) measures of the preceding context. STEPs resulting from the simulation of auditory nerve activity, yan(n, c), for the forward-speech, forward-reverberation continuum stimulus (step 00) are shown in the upper panels of Figure 4.12a for the near-near context-testcondition, and in Figure 4.12b for the far-far condition. The second and third panels for each stimulus reveal the MPR assessment of the level of reverberation in the portion of the context of 1 second duration (Z = 100 frames) immediately prior to the occurrence of the test-word. The across-channel envelope, ENVan(n), as defined by Equation 4.4, shows sharp offsets and a high dynamic range for the near-distance context and resulted in the value Rmp= 0.34. The far-distance context appears less strongly modulated, with reflected energy partially filling some of the dips in

yanylpy′lpmlpmlpyan

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Figure 4.13: Single-channel demonstration of the low-pass mask (LPM) estimation technique. A single high-frequency channel (c = 72) of the STEP is shown, highlighting the simulated auditory nerve response for the portion of context of 1 second duration (Z = 100 frames) immediately prior to the temporal location of the test-word, first in conditions of near distance reverberation (4.13a), and subsequently with far distance reverberation (4.13b). For either stimulus, the smoothed temporal envelope in the selected channel, ylp(n − τ, c), and its derivative, y⁰_lp(n − τ, c), are calculated.

Negative portions in the derivative signal create a binary mask, mlp(n, c), which locates the portions of the original signal most likely to contain the reverberation tails, mlp(n, c) yan(n, c) (cf. 4.7). The value of Rlprises from 0.63 in the near distance condition to 0.80 in the far distance condition.

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(a) STEP representations for canonical ‘sir’ and ‘stir’ templates

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sir − stir continuum

sirstir

EMSE score

(b) ‘Sir-stir’ identification using mean squared error (MSE) distance.

Figure 4.14: (Fig. 4.14a) STEP representations of the word ‘sir’ (left) and ‘stir’ (right) from the ex-treme ends of the unreverberated ‘sir-stir’ continuum. The vowel is included here for demonstration, though it is discounted in the experiments reported below. (Fig. 4.14b) Example of the EMSEscore (cf. Equation 4.11) altering across the stimuli in the continuum, quantifying the distance from ‘sir’

and ‘stir’ templates for each continuum step. Here, the first five steps of the continuum were selected as ‘sir’, whilst the remainder of the steps bring about ‘stir’ responses. The category boundary B (cf.

Equation 4.12), results in a boundary quantised to the value of 4.5.

4.4.4 ‘Sir-stir’ speech identification

A simple template-matching approach was employed for speech identification: for each sound file, the model responds with a ‘sir’ or ‘stir’ decision in much the same fashion that a human listener does. To simulate this 2AFC task, STEP templates were derived from the ‘sir’ and ‘stir’ words at either end of Watkins’

unreverber-ated ‘sir-stir’ continuum (with the efferent attenuation parameter fixed at 0 dB¹).

Resulting templates for canonical ‘sir’ and ‘stir’ test-words are shown respectively in Figure 4.14a.

During simulation, utterances for each step of the continuum were presented to the model and the corresponding STEP token, y_an^tok, was computed from the simulated auditory nerve response. The time frames corresponding to the test sound were compared in turn with the ‘sir’ and ‘stir’ templates, y_an^tem, using a standard MSE metric², given by

where y^tok_an and y^tem_an are STEPs of dimension C frequency channels and N time frames. Since listeners rely on a specific phonetic cue (the presence or absence of a [t]) in order to distinguish between ‘sir’ and ‘stir’, the template matching process was similarly restricted to the part of the test-word that contains the initial sibilant and stop³. For each utterance, the template with the smallest value of EMSE was then chosen as the test sound identity (‘sir’ or ‘stir’). This process is visualised in Figure 4.14b.

Finally, the category boundary reported the point along the 11-step continuum at which the ‘percept’ switched from ‘sir’ to ‘stir’, as shown by vertical line in Fig-ure 4.14b. By analogy to the numerical method outlined in Watkins (2005a), the category boundary B was calculated essentially from a count of the number of sir responses, Isir, with

B = Isir− 0.5 (4.12)

so that its values lie in the range -0.5 to 10.5 as used by Watkins. Since Watkins reported results across a number of human listeners, each of whom received a number of presentations of each stimulus, his category boundary results varied smoothly across the entire range. Contrasting this, the current model is quantised:

it can only output category boundaries directly at {−0.5, 0.5, 1.5, ..., 10.5}. Since the model is fully deterministic, repetitious presentation of stimuli to the model would bring about exactly the same result each time. A number of suggestions are discussed in § 4.7.3 below to work around this issue.

1In Beeston and Brown (2010), efferent attenuation was not fixed at 0 dB at this stage. Rather, templates were created using the linear-fit-derived attenuation values at each time-step.

2A similar approach, comparing unknown tokens to frozen speech templates using the MSE distance, was also used in Messing et al. (2009).

3With the benefit of hindsight, this appears overly restrictive (cf. discussion in § 4.7.3).

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Figure 4.15: STEP representations of the consonant portions of ‘sir’ (4.15a) and ‘stir’ (4.15b) from the extreme ends of the unreverberated ‘sir-stir’ continuum. A test-word from the middle of the con-tinuum is initially reported as ‘sir’ in the presence of reverberation (4.15c). When efferent attenuation is applied, the /t/ closure (triangle) is partially revealed and the word is reported as ‘stir’ (4.15d).