Informatica 30 (2006) 477-482 477
Fully Automatic Assessment of Speech of Children with Cleft Lip and Palate
Andreas Maier, Elmar Nöth and Anton Batliner
Lehrstuhl für Mustererkennung, Universität Erlangen-Nürnberg,
Martensstraße 3, 91058 Erlangen, Germany
E-mail: {Andreas.Maier,Elmar.Noeth,Anton.Batliner}@informatik.uni-erlangen.de, http://www5.informatik.uni-erlangen.de/
Emeka Nkenke
Mund-, Kiefer- und Gesichtschirurgische Klinik, Universität Erlangen-Nürnberg, Glückstraße 11, 91054 Erlangen, Germany
E-mail: Emeka.Nkenke@mkg.imed.uni-erlangen.de, http://www.mkg.uni-erlangen.de/ Maria Schuster
Abteilung für Phoniatrie und Pädaudiologie, Universität Erlangen-Nürnberg, Bohlenplatz 21, 91054 Erlangen, Germany
E-mail: Maria.Schuster@phoni.imed.uni-erlangen.de, http://www.phoniatrie.uni-erlangen.de/
Keywords: pathologic speech, automatic evaluation, children's speech Received: November 6, 2006
Cleft lip and palate (CLP) may cause functional limitations even after adequate surgical and non-surgical treatment, speech disorder being one of them. Until now, an automatic, objective means to determine and quantify the intelligibility did not exist. We have created an automatic evaluation system that assesses speech, based on the result of an automatic speech recognizer. It was applied to 35 recordings of children with CLP. A subjective evaluation of the intelligibility was performed by four experts and confronted to the automatic speech evaluation. Itcomplied with experts' rating of intelligibility. Furthermore wepresent the results obtained on a control group of 45 recordings of normal children and compare these results with those of the CLP children.
Povzetek: S programom in ljudmije analizirana razumljivost otrok z zajcjo ustnico.
1 Introduction
Cleft lip and palate (CLP) is the most common malformation of the head. It can result in morphological and functional disorders [WR02], whereat one has to differentiate primary from secondary disorders [MR01, RE02]. Primary disorders include e.g. swallowing, breathing and mimic disorders. Speech and voice disorders [SS94] as well as conductive hearing loss that affect speech development [SLS+99], are secondary disorders. Speech disorders can still be present after reconstructive surgical treatment. The characteristics of speech disorders are mainly a combination of different articulatory features, e.g. enhanced nasal air emissions that lead to altered nasality, a shift in localization of articulation (e.g. using a /d/ built with the tip of the tongue instead of a /g/ built with back of the tongue or vice versa), and a modified articulatory tension (e.g. weakening of the plosives /t/, /k/, /p/) [HG98]. They affect not only the intelligibility but therewith the social competence and emotional development of a child. In clinical practice, articulation disorders are mainly evaluated by subjective tools. The simplest method is the auditive perception, mostly performed by a speech therapist. Previous studies have shown that experience is an important factor that in-
fluences the subjective estimation of speech disorders leading to inaccurate evaluation by persons with only few years of experience [PRSS+05]. Until now, objective means exist only for quantitative measurements of nasal emissions [KSS+03, LBB+02, HD04] and for the detection of secondary voice disorders [BSM+98]. But other specific or non-specific articulation disorders in CLP as well as a global assessment of speech quality cannot be sufficiently quantified. In this paper, we present a new technical procedure for the measurement and evaluation of speech disorders and compare the results obtained with subjective ratings of a panel of expert listeners.
2 Automatic Speech Recognition System
For the objective measurement of the intelligibility of children with speech disorders, an automatic speech recognition system was applied, a word recognition system developed at the Chair for Pattern Recognition (Lehrstuhl für Mustererkennung) of the University of Erlangen. In this study, the latest version as described in detail in [Ste05] was used. The recognizer can handle spontaneous speech with
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mid-sized vocabularies of up to 10,000 words. As features we use Mel-Frequency Cepstrum Coefficients (MFCC) 1 to 11 plus the energy of the signal. Additionally 12 delta coefficients are computed over a context of 2 time frames to the left and the right side (56 ms in total). The recognition is performed with semi-continuous Hidden Markov Models (SCHMMs). The codebook contains 500 full co-variance Gaussian densities which are shared by all HMM states. The elementary recognition units are polyphones [STNE+93]. The polyphones were constructed for each sequence of phones which appeared more than 50 times in the training set.
In order to improve the recognition accuracy we applied a unigram language model. So we include just a minimum of linguistic information into the recognition process to put more weight on the acoustic features. We used two types of unigram language models according to the application scenario (cf. Section 5).
The speech recognition system had been trained with acoustic information from spontaneous dialogues of the VERBMOBIL project [Wah00] and normal children's speech. The speech data of non-pathologic children's voices (30 female and 23 male) were recorded at two local schools (age 10 to 14) in Erlangen and consisted of read texts. The training population of the VERBMOBIL project consisted of normal adult speakers from all over Germany and thus covered all dialectal regions. All speakers were asked to speak "standard" German. 90 % of the training population (47 female and 85 male) were younger than 40 years. During training an evaluation set was used that only contained children's speech. The adults' data was adapted by vocal tract length normalization as proposed in [SHSN03].
Supervised MLLR adaptation [GPW96] with the patients' data lead to further improvement of the speech recognition system. The reference transliteration was chosen according to the scenario (cf. Section 5).
3 Data
All children were asked to name pictures that were shown according to the PLAKSS test [Fox02]. This German test consists of 99 words shown as pictograms on 33 slides. With this test, the speech of children can be evaluated even if they are quite young since they do not need the ability to read. However, the children could take advantage of being able to read since the reference words were shown as subtitles. The test includes all possible phonemes of the German language in different positions (beginning, center and end of a word, cf. Figure 1).
The patients' group consisted of 35 children and adolescents (13 girls and 22 boys) with CLP at the age from 3.3 to 18.5 years (mean 8.3 ± 3.6 years). The examination was included in the regular out-patient examination of all children and adolescents with CLP. These speech samples were recorded with a close-talking microphone (dnt Call
4U Comfort headset) at a sampling frequency of 16 kHz and quantized with 16 bit. For these data no further postprocessing was done.
Furthermore a control group with 45 normal children was recorded at a local elementary school. In total, data from 27 girls and 18 boys were collected. The children were in the age from 7.4 to 10.7 (mean 9.5 ± 0.9 years). The data were collected at 48 kHz with 16 bit quantization. To match the patients' data a resampling to 16 kHz was done. For the control group a Sennheiser close-talking microphone (handgrip K3U with ME 80 head) was used. These data were post-processed: In some cases the voice of the instructor was audible on the sound track. So the instructor's voice was removed in all occasions. Furthermore all of the children's speech data was transliterated.
Informed consent had been obtained by all parents of the children prior to the recording. All children were native German speakers, some using a local dialect.
4	Subjective Evaluation
Four voice professionals subjectively estimated the intelligibility of the children's speech while listening to a playback of the recordings. A five point scale (1 = very high, 2 = rather high, 3 = medium, 4 = rather low, 5 = very low) was applied to rate the intelligibility of all individual turns. In this manner an averaged mark - expressed as a floating point value - for each patient could be calculated.
5	From Semi-automatic to Fully Automatic Evaluation
In order to measure the accuracy of a word recognizer the test data have to be transliterated completely. However, if the method should be applicable in clinical practice, this procedure is infeasible. So we tried to develop a new fully automatic evaluation method which yields similar results to the semi-automatic method reported in [SMH+06]. According to semi-automatic and the fully automatic evaluation procedures two scenarios can be formed:
In the first scenario the transliteration is available. All the data have to be transliterated in order to measure the performance of the recognizer correctly. In this case additional words appear in the transliteration which are not in the set of the reference words. The main cause for these additional words are carrier sentences like "This is a ..." (cf. reference in Table 1). So these words have to be added to the language model in order to enable their recognition. Since each word can follow each word, the probability of the target words is increased by an empirical factor of 2. Thus the size of the vocabulary changes from speaker to speaker. To attenuate this effect we could have created a single language model for all speakers containing all the words which appear in the transliteration as it was done in [SMH+06]. However, this would mean that the recogni-
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Figure 1: Pictograms of the PLAKSS test [Fox02] for the phoneme /r/ with the German target words Trecker, Zitrone, Jager (tractor, lemon, hunter)
tion results of all speakers depend on this language model. Thus, all results would have to be computed again if we add a single speaker who utters a new word to the system which did not already appear in the transliteration of the other speakers. So we chose to create an individual language model per speaker which has the disadvantage that the test set perplexity of the language model differs for each speaker.
In the second scenario—the fully automatic case—the transliteration is assumed to be unknown. Since we developed a new recording and evaluation software we now know the exact time when the reference slide was moved to the next slide. We can exploit this information to approximate a reference word chain. This reference word chain contains just the words which are shown on the slide. So we created a basic language model which was trained with just the reference words of the test since no further information is available. This model has a perplexity of 43 on the reference text. At present no garbage model was employed.
6 Evaluation Measurements
For the agreement computations between different raters on the one hand and raters/recognizer on the other hand we use the Pearson product-moment correlation coefficient [Pea96]. It allows to compare two number series which are of different scale and margin like in the given case. So the ratings of the human experts and those of the speech recognition system can be compared directly without having to define a mapping between the result of the recognizer and the experts' scores. In order to compare the raters to the recognition system the average rating of the experts was computed for each speaker. For the recognition rate of the speech recognition system we investigated the word accu-
racy (WA) like in [HSN+04], [SNH+05], [MHN+ 06], or [SMH+06] and the word recognition rate (WR). The WA is defined as
WA = C-- • 100 % R
where C is the number of correctly recognized words, -the number of wrongly inserted words and R the number
of words in the reference text. The WR is defined as
C
WR = c • 100%. R
Both measurements need a reference text in order to determine the number of correctly recognized words. However, since the reference are pictures, the text is not known a priori. One solution to this problem is to transliterate all the data like it is done in the first scenario (cf. Section 5).
Unfortunately the reference of the second scenario (cf. Section 5) is not sufficient to calculate a good word accuracy since most of the children use carrier sentences. So the carrier words are regarded as wrongly inserted words even if the recognition would be perfect. In order to avoid this problem we applied the word recognition rate instead since it does not weight the effect of inserted words. The difference between these methods is shown in Table 1.
7 Results
Since the control group was completely transliterated and recorded with our new software (cf. Section 5) we could investigate the difference between the automatic measurements and those based on the transliteration. As can be seen in Table 2 the word recognition rate correlates to both transliteration-based measurements. The automatic word accuracy, however, matches poorly with the transliteration-based measurements (cf. Table 1). Therefore we expected
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measurement	recognized word chain	reference	%
transliteration WA transliteration WR	This is moon, bucket and a a ball This is moon, bucket and a a ball	This is a moon, a bucket, and a tree This is a moon, a bucket, and a tree	55.5 66.6
automatic WA automatic WR	tiger moon bucket apple ball tiger moon bucket apple ball	moon bucket tree moon bucket tree	0 66.6
Table 1: Example of the effects of the automatic reference on the WA and WR. We assume that the spoken utterance is "This is a moon, a bucket, and a tree". Thus, the automatic reference is "moon bucket tree"
measurement	transliteration WA	transliteration WR
automatic WA automatic WR	0.40 0.60	0.21 0.60
Table 2: Correlation between the different measurements regarding the control group. The automatic WR yields the results with the best correlation to the transliteration-based measurements
5 4.5 4 3.5 3 2.5 2 1.5 1
10 20 30 40 50 60 70 80 word recognition rate
rater	avg.	# of raters
rater S	0.95	
rater M	0.92	3 raters
rater L	0.93	
rater W	0.90	
automatic WA automatic WR	-0.82 -0.90	4 raters
Figure 2: Word recognition rates in comparison to the scores of the human experts for the patient group (r = -0.90)
Table 3: Correlation r between the different raters and the automatic measurements
rates. The distribution of the patients' group shows a high variance. This is due to the fact that the patients' group contained a wide range of intelligibility. Some of the patients were as intelligible as normal children (cf. Figure 2). The correlation between the age and the word recognition rate is 0.2 for the children of the control group and 0.3 for the children of the patient group. So there is just a weak connection between the age and the recognition rate when appropriate HMM models for children are used as also observed in [GG03].
the WR to show a good agreement with the results presented in [MHN+06].
The recordings of the CLP children showed a wide range of intelligibility (see Figure 2). Subjective speech evaluation showed good consistency. The best rater achieved a correlation coefficient to the average of the other raters of 0.95. The results for the correlations of the WA, the WR and the subjective speech evaluation are shown in Table 3. When compared to the average of the raters, the WA for the recognizer has a correlation of -0.82 while the WR even correlates with -0.90. The coefficients are negative because high recognition rates come from "good" speech with a low score number and vice versa (note the regression line in Figure 2).
Figure 3 shows the word recognition rates of children in the same age range of both groups (20 patients and 45 normal children; 6 to 12 years old). As can be seen, almost all 45 children of the control group have high recognition
8 Discussion
First results for an automatic global evaluation of speech disorders of different manifestations as found in CLP speech are shown. The speech recognition system shows high consistency with the experts' estimation of the intelligibility. The use of prior information about the speech test and its setup allows us the create a fully automated procedure to compute a global assessment of the speaker's intelligibility. In difference to [MHN+06] no manual postprocessing was done. Still the experts' and the recognizer's evaluation show a high correlation.
Using a control group we could show that our measure is sufficient to differentiate normal children's speech from pathologic speech. Furthermore we could show the consistency of our new measure to the transliteration-based evaluation methods.
The technique allows an objective evaluation of speech
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patients control group distribution patients distribution control group
+ □
e g
a
14 12 10 8 6 4 2 0
30 40 50 60 70 80 word recognition rate
90
Figure 3: Distribution of the patients and the control group over the word recognition rate. Only members with about the same age were considered.
disorders and therapy effects. It avoids subjective influences from human raters with different experience and is therefore of high clinical and scientific value. Automatic evaluation in real-time will avoid long evaluation proceedings by human experts. Further research will lead to the classification and quantification of different speech disorders. This will allow to quantify the impact of individual speech disorders on the intelligibility and will improve therapy strategies for speech disorders.
9 Conclusion
Automatic speech evaluation by a speech recognizer is a valuable means for research and clinical purpose in order to determine the global speech outcome of children with CLP. It enables to quantify the quality of speech. Adaptation of the technique presented here will lead to further applications to differentiate and quantify articulation disorders. Modern technical solutions might easily provide specialized centers and therapists with this new evaluation method.
Acknowledgments
This work was supported by the Johannes and Frieda Marohn foundation at the Friedrich-Alexander University of Erlangen-Nuremberg. Only the authors are responsible for the content of this article.
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