Department of Phonetics and Linguistics


Valerie HAZAN and Andrew SIMPSON

Two sets of experiments to test the perceptual benefits of enhancing information-rich regions of consonants in natural speech were performed. In the first set, hand-annotated consonantal regions of natural VCV stimuli were amplified to increase their salience, and filtered to stylize the cues they contained. In the second set, natural semantically unpredictable sentence (SUS) material was annotated and enhanced in the same way. Both sets of stimuli were combined with speech-shaped noise and presented to normally-hearing listeners. Both sets of experiments showed statistically significant improvements in intelligibility as a result of enhancement, although the increase was greater for VCV than for SUS material. These results demonstrate the benefits gained from enhancement techniques which use knowledge about acoustic cues to phonetic contrasts to improve the resistance of speech to background noise.

1. Introduction
This paper reports some work carried out as part of a program investigating the effects of acoustic cue enhancement on the intelligibility of natural and synthetic speech. This approach aims to enhance relatively clear speech before degradation by noise, reverberation or band-pass filtering and therefore differs from conventional signal enhancement which is largely concerned with the removal of additive noise through techniques such as spectral subtraction, adaptive filtering, adaptive noise cancellation, and harmonic selection. While these methods appear to improve signal quality, they show only small increases in intelligibility (e.g., Cheng, O'Shaughnessy & Kabal, 1995).

When describing methods which enhance speech prior to degradation, a distinction must be made between those techniques which apply enhancements automatically to portions of the signal which display certain characteristics (e.g. regions characterised by fast spectral change) and those which apply enhancements to specific phonetic segments and so require the speech signal to be annotated in terms of its phonetic components.

Automatic enhancement methods such as those involving high-frequency emphasis or removal of the first formant can have a significant effect on intelligibility; however, these have shown most benefit in conditions of extreme distortion, such as infinite clipping, which have a very substantial effect on signal quality (Niederjohn & Grotelueshen, 1976). More recently, Tallal and her colleagues (Tallal et al., 1996) have applied automatic enhancement techniques which involve amplifying regions of rapid spectral change and manipulating segment durations. These were found to be beneficial in speech training with language-disordered children believed to have specific difficulty in processing sounds containing fast spectral change.

Methods in which signals are segmented and labelled using phonetic knowledge permit the manipulation of specific, perceptually-important, regions which may not be reliably identified via the types of signal processing techniques described above. Many concentrate on enhancing 'landmark' regions of the signal that are known to contain a high density of cues to phonetic identity (Stevens, 1985). These 'landmark' regions can be inherently transient and of low amplitude, such as the perceptually-important formant transitions following plosive release which are both brief and of low initial intensity as vocal fold vibration starts. Phonetically-motivated enhancement approaches have been used to increase the salience of these information-bearing regions by increasing their relative intensity or duration. By making it easier for normally-hearing listeners to process acoustic cues contained in these segments, the speech signal could become more resistant to subsequent degradation.

Techniques which enhance clear speech prior to degradation can be applied in telecommunications (e.g. telephone-based information services, or communication in noisy aircraft) where the communication channel can significantly degrade the speech signal. There are also applications in speech and language therapy and second language learning where certain perceptually-important portions of the speech signal can be emphasised in a computer-based training system to help listeners develop phonetic discrimination abilities. Jamieson (1995) used such an approach successfully in auditory training in second-language learners. These techniques have also been investigated with the view to improve speech intelligibility for listeners suffering from different types of hearing disability. For example, Gordon-Salant (1986) explored the effects of increasing consonant duration and consonant-vowel intensity ratio in a set of nonsense syllables presented to normally-hearing and hearing-impaired listeners. The manipulation of intensity ratios had the greatest effect on intelligibility. This phonetically-motivated approach has therefore clearly been successful although the need for pre-annotated material is a serious limitation in the use of these techniques.

The objectives of the work reported here are to determine the cue-enhancement strategies which are likely to have the greatest effect on intelligibility but which are also easily implemented in signal processing terms. Manipulations were primarily made to the relative intensity and the spectral shape of different portions of the signal. In the first experiment, the effect of cue-enhancement was examined using controlled nonsense Vowel-Consonant-Vowel (VCV) material which contained no contextual information. In this way, segmental intelligibility based on the perception of acoustic information alone can be evaluated. In the following experiments, similar cue-enhancement strategies were implemented in sentence-length material which exhibits a much higher degree of variability in vocalic context and degree of coarticulation.

2. VCV material enhancement

2.1 Method
36 vowel-consonant-vowel (VCV) stimuli comprising the consonants /b,d,g,p,t,k,f,v,s,z,m,z/ in the context of the vowels /a,i,u/ spoken by a male speaker were recorded and digitized at 48 kHz sampling rate with 16-bit amplitude quantization. Annotations were made manually using a waveform editing tool to segment the stimuli into different sections. The relative levels of sections of the stimuli were then manipulated before the stimuli were reassembled by abutting adjoining segments and then down-sampling the resultant stimuli to 16 kHz to smooth any waveform discontinuities at segment boundaries. Amplitude manipulations were made by calculating the mean RMS level of each segment of the stimulus; with reference to this level sample values within a segment were then scaled to either produce a relative amplitude increase, or to set the mean RMS level of a number of segments to the same value.

After manipulation, stimuli were combined with noise which had the same spectral envelope as the long-term average spectrum of speech. Signal-to-noise ratios (SNRs) of 0 and -5 dB were calculated on a stimulus by stimulus basis and took into account any change in the amplitude of the stimulus produced as a result of enhancement. The noise started 100 ms before the onset of the first vowel and finished 100 ms after the end of the second.

For all stimuli a distinction was made between (a) the transition regions between vowel and consonant, and (b) the consonantal constriction/occlusion regions, i.e. the burst transient, burst and aspiration, frication or nasality portions. For the transition portions, the problem of reduced amplitude as the consonant constriction/occlusion was formed or released was counteracted by amplifying the final five cycles of the first vowel, or the initial five cycles of the second vowel. This was done by setting the level of the first four cycles to the level of the fifth cycle. The amplitude of the consonant occlusion/constriction region was amplified by either 6 or 12 dB according to consonant category (see Table 1).



Figure 1: Waveforms of burst and formant transition regions of /d/ in



Figure 2: Spectrograms of the burst and initial transition regions in (natural and BTF conditions).

In two further conditions filtering was used to change the spectral content of perceptually-important regions in order to make them more discriminable. For plosives, the burst spectrum was examined to locate the greatest concentration of energy; the precise location varied depending on the vowel context but was around 300 Hz for labials, between 1.2 and 3 kHz for velars, and between 2.5 and 4 kHz for alveolars. The burst was then band-pass filtered to retain energy at and around this frequency with the width of the pass-band set to four times the ERB (Glasberg and Moore, 1990) at this frequency. For the fricative stimuli, the frication region was filtered to enhance the contrast in its lower-cut-off frequency, a cue to place of articulation in fricatives. The fricatives /f,v/ were high-pass and band-stop filtered respectively so that frication only appeared above 1 kHz; /s,z/ were filtered so that aperiodic energy only appeared above 4 kHz. No filtering was performed on nasal consonants. In summary, the following test conditions were used: in condition B, only the occlusion/constriction region was amplified; in condition BT, both the occlusion/constriction and format transition regions were amplified; in condition BF, the occlusion/constriction region (for plosives and fricatives) were filtered before being amplified; in condition BFT, all types of manipulations were applied.




Burst:filtered, +12dB

Burst:filtered, +12dB,
aspiration + 6dB


Friction:filtered +6dB

Friction:filtered +6dB

Nasality:+6dB Nasality:+6dB


Table 1: Manipulations applied in the VCV Experiment

2.2 Subjects
13 listeners aged between 20 and 35 with pure tone thresholds below 20 dB HL were tested.

2.3 Test procedure
Listeners were tested individually in a sound-attenuating room, using a computer-based testing procedure. Stimuli were presented binaurally via AKG240DF headphones, and listeners responded by pointing at a consonant only on the screen using a mouse. Listeners heard three blocks of each enhancement condition, and three blocks containing natural stimuli; each block contained five repetitions of the 36 stimuli. The presentation order was randomized across listeners. All listeners heard stimuli at 0 dB and -5 dB SNRs.

2.4 Results
Figure 3 shows the intelligibility scores for all conditions. ANOVAs revealed that the effect of test condition was significant at -5 dB SNR [F(4,48)=41.54; p<0.0001] and at 0 dB SNR [F(4,48)=16.04, p<0.0001]. At both SNRs all enhanced conditions gave significantly higher intelligibility scores than the natural condition. Filtering combined with amplitude manipulations did produce a significant additional improvement at the worse SNR. The highest mean increase was 12% for -5 dB SNR and 6% for 0 dB SNR. The scores obtained for the BFT condition at -5 dB were nearly identical to those obtained for the unenhanced stimuli at 0 dB SNR. The effect of the enhancement therefore corresponds to an increase in signal-to-noise ratio of approximately 5 dB. The main effects of subject and vocalic context were also significant at both SNRs. Duncan's Multiple Range tests revealed that consonant perception in the /u/ context was significantly poorer than in the /i/ and /a/ contexts (see Figures 4 and 5).

Figure 3: Mean consonant intelligibility scores for different enhancements in the VCV experiment.

Information Transfer analyses were applied in order to determine how well consonants were recognised in terms of their voicing, place and manner of articulation, and how the enhancements applied affected the correct labelling in terms of these features. ANOVAs were then carried out on these voicing, place and manner scores. The pattern of errors obtained is consistent with what is known about consonant perception in noise. The voicing feature was robust and was well preserved in conditions of noise degradation. However, consonants were most often confused in terms of their place of articulation, and to a lesser extent, in terms of their manner of articulation. Enhancements led to a significant increase in the correct perception of the place and manner of articulation.

Results are presented here for the BFT condition, where all types of enhancements were applied. Results are presented separately for the consonants in the context of the vowels , /i/ and /u/. It can be seen that recognition was poorest overall for consonants in the context of /u/ and that the same general patterns of errors are seen at both SNRs. In all three contexts, the greatest effect of the enhancements applied is in the correct recognition of the consonants' place of articulation (20% improvement in the context of ). Manner discrimination also improved slightly, which is likely to be due to a reduction in plosive/fricative confusions.

Figure 4: Correct identification of features of voicing, place and manner of articulation for consonants presented in three vocalic contexts at an SNR of -5 dB for natural and BFT conditions.

Figure 5: Correct identification of features of voicing, place and manner of articulation for consonants presented in three vocalic contexts at an SNR of 0 dB for natural and BFT conditions.

The data were analysed further to see which specific consonants benefited the most from the enhancement strategies applied. Bar charts showing the percentage of correct responses per consonant are presented in figures 4 and 5. Overall, it can be seen that all but one consonant (/g/) either showed higher or similar scores in the enhanced condition relative to the control condition. As expected, the correct identification of place of articulation of voiced plosives was particularly difficult in noise. At SNR -5 dB, /d/ identification showed a dramatic improvement after enhancement.

Figure 6: correct identification of natural and enhanced (BFT condition) consonants at SNR -5 dB.

Confusion matrices for the data reveal which confusions occuring in the natural condition were disambiguated once enhancement strategies have been applied. At 0 dB SNR, a number of confusions are seen within the voiceless stop (/p/-/k/) and the voiced stop classes. The nasals /m/ and /n/ are also frequently confused, as are the fricatives /f/ and /s/. Once enhancements were applied, only the nasal confusions and voiced-stop confusions remained. The enhancements therefore had the greatest effect in disambiguating voiceless stops, and voiceless fricatives. A similar pattern was seen at SNR -5 dB.

3. Sentence material enhancement
Many studies evaluating the effect of enhancement have used nonsense VCV, CV or CVC syllables (e.g., Gordon-Salant, 1986). This is a necessary step as it is only possible to analyse the effect of enhancement in stimuli in which the perceptual contribution of contextual information has been eliminated. However, the greater degree of coarticulation and the greater variety in vocalic context seen in sentence-level material may strongly affect the perceptual effect of enhancements. It is therefore important to test enhancement strategies with more natural sentence-length material whilst still controlling the contribution of contextual information.

3.1 General Method
The second set of experiments applied similar enhancement techniques to natural sentence materials. 50 semantically-unpredictable sentences (SUS) (Benoit, Grice and Hazan, 1996), read by the same male speaker as in the VCV experiment, were recorded and digitized at 16 kHz with 16 bit amplitude quantization. SUS material was used in order to limit the amount of contextual information present; sentences were syntactically correct but had words with no semantic relationship. They were constructed using five different grammatical structures, and each sentence contained four key words. Examples of SUS sentences are presented in Appendix I. A greater range of consonants including affricates and approximants was manipulated than in the VCV experiment; consonants annotated were . Sentences were annotated to identify the consonant constriction/occlusion and transition regions as described in the VCV experiment.

3.2 Sentence Experiment 1

3.2.1 Method
Following informal listening experiments with the SUS material enhanced in the same way as in VCV Experiment 2, some small adjustments were made to the enhancement strategies. Plosive and affricate bursts were filtered, but it was necessary to use wider pass-bands given the greater variation in centre burst frequency in this sentence-length material. The degree of amplification of the burst was reduced to 9 dB. Amplification was also applied to the aspiration segments in the voiceless stops. No filtering was applied to fricatives due to the increased variability in cut-off frequency in these phones in sentence material. In the formant transition regions, the five final and initial voicing cycles before and after the consonant occlusion/constriction region were boosted by 3 dB. After being manipulated, stimuli were combined with speech-shaped noise at 0 dB and 5 dB SNR.

In addition, in order to check the effect of these small changes in enhancement levels, the same manipulations that were used in the SUS material were also applied to the VCV material described above.
Plosivesburst: +9dB,filtered; aspiration: +9dB; transitions+
Fricativesfriction: +6dB; transitions+
Affricatesburst: +9dB, filtered; friction: +6dB; transitions+
Approximantsconstriction: +3dB; transitions+
Nasalsnasality: +6dB; transitions+

Table 2: Manipulations applied in Sentence Experiment 1.

Figure 6: Time-amplitude waveforms of the SUS sentence "The bright bit raised the weight" in natural and enhanced conditions.

3.2.2 Subjects
Separate groups of listeners were used for each SNR condition on order to avoid word learning effects. All were aged between 20 and 35 with pure tone thresholds below 20 dB HL. 12 listeners were tested in the 0 dB SNR condition and 13 in the 5 dB SNR condition.

3.2.3 Test procedure
Listeners were tested individually in a sound-attenuating room, using computer-controlled sentence presentation. Sentences were presented binaurally via AKG240 DF headphones, and listeners responded by writing down the sentence heard on a response sheet. Each listener heard 25 SUS sentences in the natural condition and 25 in the enhanced condition. Sentence order within a block was randomized, and which half of the sentence list a block was drawn from, and whether a subject heard the enhanced or natural condition first were counterbalanced across subjects.

3.2.4 Results
Sentences were scored in terms of the number of key-words correctly transcribed. Intelligibility scores were then obtained by calculating the percentage of key-words correctly transcribed in each 25-sentence block (total of 100 key-words). Figure 7 shows the intelligibility scores for all conditions.

At 5 dB SNR, the effect of enhancement was significant [F(1,8)=6.08, p=0.039]. The order in which conditions were presented, and the sentence blocks used did not significantly affect test scores. At 0 dB SNR, the enhanced condition did not produce significantly higher scores than the natural condition. Results obtained for the VCV tests replicated those obtained above. At 0 dB SNR, mean intelligibility scores showed a significant increase from 76% to 83% (paired-difference t-test p<0.001) as compared to an increase from 77% to 83% in VCV Experiment 2.

Figure 7: Intelligibility scores for Sentence Experiment 1

Little benefit of cue-enhancement on intelligibility was obtained for this sentence material. This experiment varied from previous one in three important respects: First, the type of material itself was radically different: the sentence-length material imposed a greater cognitive load on the listeners, especially as the sentences used were semantically-unpredictable. Second, a wider range of consonant classes with a greater variety of vocalic contexts were manipulated compared to previous experiments. Third, a different set of subjects was tested.

The replication of previously-obtained VCV results with a different listener group makes it unlikely that listener effects might be the cause for this difference. A detailed examination of sentence results did suggest that some of the enhancements made to affricates and approximants had led to an increased number of errors for words containing those sounds. In order to test whether this was the cause of the poorer results compared with those obtained for the VCV material, a further experiment was set up using the same SUS material, but with manipulations made only to plosives, fricatives and nasals, as in the VCV experiment.

3.3 Sentence Experiment 2

3.3.1 Method
Further adjustments were made to the enhancement techniques used in sentence experiment 1. First, bursts were no longer filtered as it was found that the filter-bandwidths could not be reliably set due to the greater variability in burst center frequency in continuous speech. The degree of amplification of the burst and aspiration was also changed relative to sentence experiment 1. Second, a change was made in the way in which the initial and final vocalic cycles were amplified to avoid discontinuities in the speech signal; vocalic cycles were amplified by between 4 and 2 dB; amplification was gradually altered with the cycles nearest the occlusion being given the greatest amplification. After being manipulated, stimuli were combined with speech-shaped noise at 0 dB SNR.
Plosivesburst: +12dB; aspiration: +6dB; transitions+ 4-2 dB
Fricativesfriction: +6dB; transitions+ 4-2 dB
Affricatesnot manipulated
Approximantsnot manipulated
Nasalsnasality: +6dB; transitions+4-2 dB

Table 3: Manipulations applied in Sentence Experiment 2.

b. Subjects
12 listeners were tested. All were aged between 20 and 30 with pure tone thresholds below 20 dB HL.

c. Test procedure
As in Sentence Experiment 1.

Mean scores are presented in Figure 8. The effect of enhancement was significant [F(1,8)=19.66, p=0.002]. The effect of order of presentation was not significant but there was significant interaction between order of presentation and enhancement [F(1,1)=21.45; p=0.002]: listeners who heard the enhanced sentences second showed a greater increase in intelligibility scores, but this effect did not apply when the presentation order was reversed. Learning effects with SUS material have also been reported in other studies (e.g. Grice and Hazan, 1989).

Figure 8: Intelligibility scores for Sentence Experiment 2.

3.4 Sentence material discussion
The extension of enhancement techniques from highly-controlled VCV material to sentence-level material did lead to a need for refinements of the enhancement strategies. This was due to the fact that consonants appeared in a much wider variety of vocalic contexts and were also inherently more variable in their spectral and temporal characteristics; as a result, the types and levels of enhancements which were appropriate in the VCV experiments sometimes led to abrupt changes in amplitude and other discontinuities in the sentence material which had a deleterious effect on intelligibility. Results obtained in Experiment 2, however, showed that more careful adjustments made to the degree of amplification of certain constriction/occlusion and transition portions did lead to significant increases in sentence intelligibility as a result of cue-enhancement.

4. Conclusion
The work reported here has shown the benefit of speech pattern enhancements in improving perception by normally-hearing listeners in poor listening conditions. Despite the relatively gross manipulations made to the stimuli in this study, a significant improvement in intelligibility was achieved both for VCV and sentence material. These enhancement techniques are currently being implemented within a diphone-based text-to-speech system to allow testing of the enhancement technique on the unlimited range of utterances that can be generated in this way.

5. Acknowledgements
This work was funded by an EPSRC project grant (GR/J10426).

6. References
Benoit, C., Grice, M. and Hazan, V. (1996) The SUS test: a method for the assessment of text-to-speech synthesis intelligibility using Semantically Unpredictable Sentences, Speech Communication 18: in press.

Cheng, Y.M., O'Shaughnessy, D. & Kabal, P. (1995) Speech enhancement using a statistically derived filter mapping. Proceedings of International Conference of Spoken Language Processing, Banff, October 1992, vol.1, 515-518.

Glasberg, B.R. and Moore, B.C.J. (1990) Derivation of auditory filter shapes from notched-noise data. Hearing Research, 47, 103-138.

Gordon-Salant, S. (1986) Recognition of natural and time/intensity altered CVs by young and elderly subjects with normal hearing. Journal of the Acoustical Society of America, 80, 1599-1607.

Jamieson, D.G. (1995) Techniques for training difficult non-native speech contrasts. Proceedings of the XIIIth International Congress of Phonetic Sciences, 4, 100-104.

Nagarajan, S.S., Wang, X., Merzenich, M.M., Schreiner, C.E., Jenkins, W.M., Johnston, P.A., Miller, S.L., Byma, G. & Tallal, P. (1995) Speech modification algorithms for training language-learning impaired children. Proceedings of Society of Neuroscience Conference, 1995.

Niederjohn, R.J. & Grotelueschen, J.H. (1976) The enhancement of speech intelligibility in high noise levels by high-pass filtering followed by rapid amplitude compression, IEEE Trans. ASSP-24, p277.

Stevens, K.N. (1985) Evidence for the role of acoustic boundaries in the perception of speech sounds. In V. Fromkin (ed) Phonetic Linguistics: Essays in the honor of Peter Ladefoged. Academic Press, Orlando.

Tallal, P., Miller, S.L., Bedi, G., Byma, G., Wang, X., Nagarajan, S., Schreiner, C., Jenkins, W., Merzenich, M. (1996) Language comprehension in language-learning impaired children improved with acoustically modified speech. Science, 271, 81-84.

© 1996 Valerie Hazan and Andrew Simpson

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