First formant frequencies also appeared to be influenced by the presence of noise for at least one speaker. For SC, F 1 frequencies were higher for vowels from utterances produced in the three noise conditions than for vowels produced in the quiet. There was little change in F 2 frequencies across noise conditions for either speaker. The present results demonstrated several clear differences in the acoustic characteristics of speech produced in quiet compared to speech produced in noise. We carried out two separate perceptual experiments to verify their earlier conclusions.
In experiment I, subjects identified utterances from the quiet condition and the dB masking noise condition in a forced-choice identification task. Subjects were 41 undergraduate students who participated to fulfill a requirement for an introductory Psychology course. All subjects were native English speakers and reported no previous history of a speech or hearing disorder at the time of testing. Stimulus materials were the tokens of the digits zero through nine, produced in quiet and 90 dB of masking noise by both talkers.
All stimuli were equated in terms of overall rms amplitude using a program that permits the user to manipulate signal amplitudes digitally Bernacki, Stimuli were presented via a bit digital-to-analog converter over matched and calibrated TDH headphones. Wideband noise, filtered at 4. The dB SPL masking noise was presented over the headphones 1 s later. A randomly selected stimulus was presented for identification ms following the onset of masking noise.
Masking noise was terminated ms following stimulus offset.
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Subjects responded by depressing one of the ten digit keys on the terminal keyboard. Subjects were presented with two blocks of experimental trials. Within each block, each of the utterances was presented once. All test utterances were presented to each subject. Thus talker MD or SC and masking noise condition quiet or 90 dB were manipulated as within-subjects factors. Intelligibility of words produced in quiet and 90 dB of masking noise experiment I. As shown in Fig. This pattern was observed for both talkers and for both the quiet and dB noise conditions.
In experiment I, digits produced in noise were recognized more accurately than digits produced in quiet. The consistency of this effect in experiment I is quite remarkable given that the stimuli were drawn from a very small, closed set of highly familiar test items. To verify that the results of experiment I were reliable and could be generalized, we replicated the experiment with a different set of stimuli drawn from the original test utterances.
Experiment II was carried out with stimuli taken from the dB masking condition. That is, the replication used the stimuli from the quiet and dB masking conditions. All subjects were native speakers of American English and met the same requirements as those used in the previous experiment. All other aspects of the experimental procedure were identical to those of experiment I.
The results of experiment II are shown in Fig. Intelligibility of words produced in quiet and dB of masking noise experiment II. Comparing the data shown in Figs. The results of the ANOVA performed on the data from this experiment also replicate the results of the previous experiment. Each of the significant effects involving the masking noise variable reported in experiment I was also replicated.
Utterances produced in dB of noise were more accurately identified than utterances produced in the quiet. Also replicating the results of experiment I, the effect of masking noise on performance was greater for talker MD than for talker SC. In their earlier research, as in each of the perceptual experiments reported here, subjects were more accurate at identifying utterances originally produced in noise than utterances produced in quiet.
Thus differences in the acoustic—phonetic structure of utterances produced in quiet and utterances produced in noise had reliable effects on intelligibility. The results of the present acoustic analyses demonstrate reliable and consistent differences in the acoustic properties of speech produced in quiet environments and environments containing high levels of masking noise.
The differences we observed in our analyses were not restricted only to the prosodic properties of speech such as amplitude, duration, and pitch, but were also present in measurements of vowel formant frequencies. Moreover, for both talkers, we observed substantial changes in the slopes of the short-term power spectra of vowels in these utterances, which were shifted upward to emphasize higher frequency components.
The changes in amplitude, fundamental frequency, and duration reported here were often fairly small across the different noise levels. In particular, in comparing the dB and dB conditions, the change in amplitude was about 2 dB for each speaker. This 2-dB increase in the face of a dB increase in masking noise is much smaller than would be predicted from previous research. Research using communication tasks involving talker—listener pairs have generally reported a 5-dB increase in signal amplitude for each dB increase in noise Lane et al.
The smaller differences observed in the present study suggest that masking noise may have a greater influence on speech in interactive communication tasks involving talker—listener pairs than in noninteractive tasks, such as the one used here, where no external feedback is available. Despite the magnitude of the observed differences, the findings are reliable and demonstrate important changes in speech produced in various noise conditions.
Apparently, several acoustic characteristics of speech produced in noise, above and beyond changes in rms amplitude, make it more intelligible in a noisy environment than speech produced in the quiet. The present findings replicate several gross changes in the prosodic properties of speech which have been previously reported in the literature Hanley and Steer, ; Draegert, For one of our two speakers, the results also demonstrate a clear influence of masking noise on the formant structure of vowels.
We believe that the present results have a number of important implications for the use of speech recognition devices in noisy environments and for the development of speech recognition algorithms, especially algorithms designed to operate in noise or severe environments. In the recent past, a major goal of many speech scientists and engineers working on algorithm development has been to improve recognition of speech in noise Rollins and Wiesen, Most efforts along these lines have involved the development of efficient techniques to extract speech signals from background noise Neben et al.
Other efforts have attempted to solve the speech-in-noise problem by developing procedures that incorporate noise into the templates that is similar to the noise in the testing environment Kersteen, By this technique, the signal does not have to be extracted from the noise; rather the entire pattern containing signal and noise is matched against the stored template.
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This second technique, of incorporating noise into the templates, is accomplished by training the speech recognizer in a noisy environment so that noise along with speech is sampled on each trial. Kersteen reported success with this method of training; the highest recognition performance was produced when training and testing occurred in the same noise environment.
Kersteen interpreted these results as demonstrating the importance of incorporating noise into the templates when noise is also present at testing. An alternative explanation for the success of this training method is that the templates produced in noise capture acoustic characteristics of speech produced in noise that differ from those of speech produced in quiet. Unfortunately, little, if any, attention has been devoted to examining the changes in the speech signal that occur when a talker speaks in the presence of masking noise.
The present findings demonstrate reliable differences in the acoustic—phonetic structure of speech produced in quiet versus noisy environments. Because of these differences, the problem of speech recognition in noise is more complicated than it might seem at first glance. The problem involves not only the task of identifying what portion of the signal is speech and what portion is noise but it also involves dealing with the changes and modifications that take place in the speech signal itself when the talker produces speech in noise.
Any speech recognition algorithm that treats speech as an arbitrary signal and fails to consider the internal acoustic—phonetic specification of words will have difficulty in recognizing speech produced in noise. This difficulty should be particularly noticeable with the class of current algorithms that is designed around standard template matching techniques. These algorithms are, in principle, incapable of recovering or operating on the internal acoustic—phonetic segmental structure of words and the underlying fine-grained spectral changes that specify the phonetic feature composition of the segments of the utterance.
Even if dynamic programming algorithms are used to perform time warping before pattern matching takes place, the problems we are referring to here still remain. Factors such as changes in speaking rate, masking noise, or increases in cognitive load may affect not only the fairly gross attributes of the speech signal but also the fine-grained segmental structure as well. Moreover, as far as we can tell, changes in speaking rate, effects of noise, and differences in cognitive load, to name just a few factors, appear to introduce nonlinear changes in the acoustic—phonetic realization of the speech signal.
Thus simple linear scaling of the speech will not be sufficient to capture rate-related changes in the acoustic—phonetic structure. The present findings are also relevant to a number of human factors problems in speech recognition. Both of the speakers examined in this study adjusted their speech productions in response to increased masking noise in their environment. These adjustments made the speech produced in noise more intelligible than speech produced in quiet when both were presented at equal amplitudes in a noisy environment.
The speakers appeared to automatically adjust the characteristics of their speech to maintain intelligibility without having been explicitly instructed to do so. Presumably, the increase in intelligibility would have been at least as great if the speakers had been given such instructions. Given the currently available recognition technology, it should be possible to train human talkers to improve their performance with speech recognizers by appropriate feedback and explicit instructions. In this regard, the present findings are related to several recent investigations in which subjects received explicit instructions to speak clearly Chen, ; Picheny et al , ; Picheny et al , Many of the differences they identified are similar to those reported here.
Specifically, longer segment durations, higher rms amplitudes, and higher F 0 values were reported for clear speech versus conversational speech. These changes in amplitude, duration, and pitch are also characteristic of speech that is deliberately emphasized or stressed by the talker Lieberman, ; Klatt, ; Cooper et al , Thus clear speech, emphasized or stressed speech, and speech produced in noise all tend to show increases in these three prosodic characteristics.
The pattern of formant data shows less similarity between speech produced in noise and clear speech or emphasized stressed speech. Chen reported that in clear speech F 1 and F 2 moved closer to target values. This movement enlarges the vowel space and makes formant values for different vowels more distinct, a pattern that is also characteristic of stressed vowels Delattre, Our vowel formant data do not display this pattern of change.
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In the present study, masking noise produced increases in F 1 frequency for speaker SC but had little effect on formant frequencies for MD. Thus it appears that the presence of masking noise did not produce the same qualitative changes in production as instructions to speak clearly or to stress certain utterances.
While several parallel changes occur in each case, a number of differences are also present in the data. Increases in fundamental frequency, vowel duration, and F 1 frequency have all been reported for shouted speech Rostolland and Parant, ; Rostolland, a , b. In addition, spectral tilt is reduced in shouted speech Rostolland, a. Each of these findings is in agreement with the present data for speech produced in noise.
Thus it appears that the differences between speech produced in quiet and speech produced in noise are similar in kind to the differences between spoken and shouted speech. However, for each of the variables mentioned above, the differences between shouted and spoken speech are greater in magnitude than the present differences between speech produced in quiet and speech produced in noise. It would, therefore, be reasonable to expect that shouted speech should also be more intelligible than conversational speech in similar circumstances.
However, the literature reports exactly the opposite result: While our talkers were able to increase the intelligibility of their speech by making changes in speech production that appear similar in kind to those reported for shouted speech, the magnitude of these changes is much greater in shouted speech. The extreme articulations that occur in shouted speech apparently affect intelligibility adversely, perhaps introducing distortions or perturbations in the acoustic realizations of utterances Rostolland, a , b.
In addition to the recent work of Picheny et al. Instructions to talk loudly, articulate more precisely, and talk more slowly have been shown to produce reliable gains in speech intelligibility scores when speech produced under adverse or noisy conditions is presented to human listeners for perceptual testing see, for example, Tolhurst, , Unfortunately, at the present time, we simply do not know whether these same training and feedback techniques will produce comparable improvements in performance with speech recognition systems.
It is clearly of some interest and potential importance to examine these factors under laboratory conditions using both speech recognizers and human observers. If we knew more precisely which acoustic—phonetic characteristics of speech spectra separate goats from sheep, we would be in a better position to suggest methods to selectively modify the way talkers speak to speech recognizers through training and directed feedback see Nusbaum and Pisoni, We consider this to be an important research problem that has been seriously neglected by engineers and speech scientists working on the development of new algorithms for speech recognition.
The human talker is probably the most easily modified component of a speech recognition system. In addition to being the least expensive component to change or modify, it is also the most accessible part of the system. Thus substantial gains in performance in restricted task environments should be observed simply by giving talkers directed feedback about precisely how they should modify the way they talk to the system.
We should qualify these remarks by also noting that these expected gains in performance can only be realized by additional basic research on how humans talk to speech recognizers under a wider variety of conditions. The results reported in the present article demonstrate that talking in the presence of masking noise not only affects the prosodic aspects of speech but also the relative distribution of energy across the frequency spectrum and the fine-grained acoustic—phonetic structure of speech as revealed in the formant frequency data. If we knew more about the role of feedback in speech production, and if we had more knowledge about the basic perceptual mechanisms used in speech perception, we would obviously have a much stronger and more principled theoretical basis for developing improved speech recognition algorithms specifically designed around general principles known to affect the way humans speak and listen.
The present investigation has a number of limitations that are worth discussing in terms of generalizing the findings beyond the present experimental context. First, we used isolated words spoken in citation form. It is very likely that a number of additional and quite different problems would be encountered if connected or continuous speech were used for these tests. The effects we observed with isolated words may be even more pronounced if the test words are put into context or concatenated together into short phrases or sentences. Overview Getting started Overview Making choices about communication Decisions Decisions A quick guide to communication.
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Creating a good listening environment. The three main barriers to understanding speech for anyone with a hearing loss are: Distance from the sound Background noise Reverberation Distance from the sound Loudness and pitch of sound, as well as the distance from the sound, are all important in our ability to hear.
There is reason to suspect the effect may be much greater on children with hearing loss. Ross is a noted audiologist who also has the benefit of firsthand experience as a hearing aid user for over 40 years. Ross comments, "The auditory distortions produced by the hearing loss produce an equivalent of 15 dB noise in the environment. It is as if suddenly the noise in the environment has increased by 15 decibels. In my humble opinion, the social ramifications of inappropriate acoustics may carry a very significant, and largely unrecognized, economic toll.
Students with hearing loss can no longer be considered a low incidence group. What a great opportunity we have! If you discuss the article with acousticians, you are likely to hear terms like near-field, far-field, reverberant field, Lombard effect, critical distance and reverberation radius. Acoustics is a complex field and what I have shared is based on one key principle, and, at that, is interpreted by a layman.
The experts may determine the effective potential increase in Comprehension Distance resulting from improved acoustics is less, and perhaps considerably less, than the idealized numbers presented here, for a variety of reasons. Indeed, the number is apt to vary with the particular acoustical criteria selected for the classroom and with the characteristics of the individual student's hearing loss.
I suspect, however, whatever the number may be, the potential social benefits may be quire significant and go a great distance to remove most, if not all, of the barriers discussed here. In closing, for all the reasons cited above, in my opinion, appropriate acoustics should be considered an extremely high priority. What we can know for sure is that a growing body of quantitative research, along with increasing precedents from case law, support the need for schools to provide acoustically-adapted classroom environments as an appropriate accommodation for mainstreamed deaf and hard of hearing students who depend on listening as a means of communication access.
He's a staunch advocate for his teenage son, an academically gifted student who is hard of hearing. Of this article, leading professionals in the field have commented, " I think that your article is terrific! Even if your figures may not exactly apply in all circumstances, they certainly drive home the point. Carol Flexer, quoted in article. It is definitely worth pursuing. Incidental learning overhearing is usually either ignored or totally underestimated.
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The distance between the speaker and the hearing impaired listener is often the greatest obstacle to speech understanding. Because the intensity of a sound decreases inversely with the square of the distance to its source, the sound pressure decreases by 6 dB when the distance between the speaker and listener is doubled. Because the ambient noise level remains constant, the SNR - and therefore speech intelligibility - worsens as the distance between the speaker and the listener increases.
Clarity of speech understanding is directly related to the distance between the speaker and the listener. In a fairly quiet restaurant or classroom, the ambient noise level can be around 60 dB. A speaker with a normal voice generates a sound pressure level of 65 dB at a distance of 1 meter. If the distance between the speaker and the listener is doubled twice, to 4 meters about 12 feet , the sound pressure level will have dropped to 53 dB, which is 7 dB less than the background noise level.
In other words, the background noise totally "drowns out" the speaker's voice. New York , New York.