As we mention in section 2.1, the annotations by the 10 experts may result in a differ-ent prosodic structure than when they actually read the text aloud. We will validate the reference transcription through comparison of the annotated and spoken versions of three experts to rule out that there exists a considerable discrepancy between the annotated and spoken versions.
Spoken reference
A production task was performed to obtain the spoken versions from the text. Three of the ten experts mentioned above were asked to read the two newspaper articles aloud.
From these spoken versions of the texts 20 sentences were selected (the same sentences as mentioned in section 2.2.1). These sentences were analyzed to obtain the prosodic structures that the speakers realized. The analysis consisted of two parts: (i) pitch con-tour analysis together with auditory analysis to indicate which words were accented, and (ii) analysis in the time domain together with auditory analysis to indicate at which junctures phrase boundaries were realized. The strength of the phrase boundary was determined on the basis of pause duration, melodic aspects (such as continuation rise) and segmental factors (such as phrase final lengthening). An accent is assigned if there is an accent lending rise or fall in the pitch contour. A weak boundary is assigned if there is no pause or a short pause, associated with a continued high pitch and/or phrase final lengthening. A medium boundary is assigned if there is a 200–500ms pause, associated with a continuation rise and/or phrase final lengthening. A strong boundary is assigned if there is a pause with a duration longer than 500ms, associated with a continuation rise and/or phrase final lengthening.
As mentioned in section 2.2.1, there should be a reasonable level of agreement between experts to be allowed to compute a mean representation. No conspicuous differences were found in the numbers of accents and phrase boundaries allocated by the individ-ual speakers (see Table 2.2). To assess the agreement, we computed the kappa coeffi-cient again. For allocation of phrase boundaries K = 0.73 and for allocation of accents K = 0.77. This means that the agreement between the three experts was satisfactory (see section 2.2.1). Therefore we consider it valid to compute a mean spoken reference.
Table 2.2: Number of accents and phrase boundaries for each expert and the mean spoken reference.
boundaries
expert weak medium strong total accents
04 16 16 21 53 130
05 17 14 21 52 129
08 23 11 21 55 133
reference 16 13 21 50 131
The distribution of boundaries corresponds with that described in section 2.2.1. How-ever, as the spoken versions are produced by only three experts, we had to adjust the criteria for phrase boundary strength and accent. In this mean spoken reference a word is marked for accent when the score for that word was 2 or 3. The criteria for distribu-tion of phrase boundaries are given below.
0 - 1 no boundary 2 - 4 weak boundary 5 - 7 medium boundary 8 - 9 strong boundary
Annotated versus spoken versions
For the three speakers the prosodic structures of their spoken versions of the 20 sen-tences from the newspaper articles were compared to the prosodic structures of their own annotations of the same 20 sentences. Comparing the numbers of phrase bound-aries and accents for the spoken versions (Table 2.2) to those for the annotations (Ta-ble 2.1), we see that there is no large discrepancy in the number of phrase boundaries and accents between the reference transcription and the spoken reference. These re-sults give a first impression of the capability of speakers to predict on paper which prosodic structure they would assign when they actually read the text aloud.
To obtain a more revealing view on the performance, more fine-grained measures were applied. We computed the accuracy, precision, recall and Fβ-value (van Rijsbergen, 1979), which are measures typically used in the Information Retrieval domain.
Accuracyis the fraction of predictions that are correct. Precision is a measure of the ratio between hits and incorrect insertions (or false alarms). Recall is a measure of the ratio between hits and incorrect omissions (or misses). The Fβ-valueis a measure combining precision and recall.
Table 2.3: Computation of accuracy, precision and recall.
reference accent no accent
prediction accent A B
no accent C D
Table 2.3 and equations 2.2–2.4 show how the measures accuracy, precision and re-call are computed for accents. For phrase boundaries the performance measures are computed in a similar way.
accuracy = (A + D)
(A + B + C + D) (2.2)
2.2 REFERENCE TRANSCRIPTION
precision = A
(A + B) (2.3)
recall = A
(A + C) (2.4)
In these equations B denotes insertions and C denotes omissions. The precision be-comes higher as the number of insertions decreases. The recall bebe-comes higher as the number of omissions decreases.
The Fβ-value is computed with equation 2.5.
Fβ = ((β2+ 1) ∗ prec ∗ rec)
(β2∗ prec + rec) (2.5)
If β = 1 the precision and recall have the same weights. If β is chosen zero, then the Fβ-value equals the precision. Since we assume that precision and recall are of equal importance here, the assumption β = 1 was made.
Phrase boundaries
Precision and recall are measures for bimodal values (zero or one; present or absent).
Because there are several boundary strengths, the computation of these performance measures for phrase boundaries is somewhat less straightforward. We had to find a way to derive a bimodal value from the existing four-modal value for boundaries (no boundary, weak, medium or strong boundary).
Confusion matrices were computed per expert (see Table 2.4). From these we derived a bimodal value for phrase boundaries, insertions and omissions according to two meth-ods. One method does not take into account the boundary strength (it only makes a distinction between boundary and no boundary). However, there is a large difference between strong boundaries and weak boundaries. For phrase boundaries a standard consistency criterion is agreement within +/- 1 level (Pitrelli et al., 1994). Therefore, we used a second (rather stringent) method that does take into account boundary strength (when the system assigns a lower boundary than the experts did, we call it a quasi omission, when the system assigns a higher boundary than the experts did, we call it a quasi insertion). Quasi omissions and quasi insertions are added up to the real omissions and insertions.
We computed the performance measures according to both methods. The first method shows to what extent speakers are able to predict where they would produce a phrase boundary. The second method is even more exact, it shows to what extent speakers are able to predict where they would produce a phrase boundary and what the boundary strength would be.
Table 2.4: Confusion matrices per expert for allocation of phrase boundaries, comparing the annotations on paper with their spoken versions.
expert04 annotation
no weak medium strong
no 301 6
spoken weak 6 7 3
medium 2 8 6
strong 1 20
expert05 annotation
no weak medium strong
no 301 6 1
spoken weak 11 2 3
medium 3 3 8
strong 1 20
expert08 annotation
no weak medium strong no 290 15
spoken weak 5 13 5
medium 11
strong 1 20
Table 2.5 gives the performance measures for allocation of phrase boundaries for the three speakers. For computation of these measures the annotations were taken as ref-erence and the spoken versions as test case (as in Table 2.3).
Table 2.5: Performance measures per expert for allocation of phrase boundaries, comparing annotations on paper with their spoken versions.
method 1 method 2
accuracy precision recall Fβ=1 accuracy precision recall Fβ=1
E04 96 81 85 83 93 66 79 72
E05 94 73 84 78 92 73 63 67
E08 94 91 77 83 93 88 69 77
When we consider method 1 with respect to phrasing, the results show that the spoken versions of the sentences correspond rather well with the speakers’ annotations of the sentences. The performance measures for expert 05 are somewhat lower than those for expert 04 and expert 08, but are still reasonably good. This means that speakers are capable of predicting where they would allocate phrase boundaries when reading text aloud.
2.2 REFERENCE TRANSCRIPTION
When we consider method 2, the performance measures are somewhat less promising.
The measures for expert 08 are still reasonably good, but the measures for expert 04 and expert 05 are worse. This means that though speakers are capable of predicting at which junctures they would allocate phrase boundaries, there is less agreement in predicting the boundary strength.
Accents
Accent is a bimodal value (accent or no accent), thus the computation of the perfor-mance measures is straightforward. We again computed confusion matrices for the three speakers (see Table 2.6).
Table 2.6: Confusion matrices per expert for allocation of accents, comparing annotations on paper with their spoken versions.
expert04 annotation
accent no accent
spoken accent 116 14
no accent 20 210
expert05 annotation
accent no accent
spoken accent 101 28
no accent 28 203
expert08 annotation
accent no accent
spoken accent 119 14
no accent 19 208
Table 2.7 gives the performance measures for the allocation of accents for the three speakers. Again, the annotations were taken as reference and the spoken versions as test case. With respect to accentuation, the results show that the spoken versions of the sentences correspond rather well with the speakers’ annotations of the sentences.
As for allocation of phrase boundaries, the performance measures for expert 05 are somewhat lower than those for expert 04 and expert 08, but are still reasonably good.
This means that speakers are capable of predicting to which words they would assign accents when reading text aloud.
Table 2.7: Performance measures per expert for allocation of accents, comparing annotations on paper with their spoken versions.
accuracy precision recall Fβ=1
E04 91 89 85 87
E05 84 78 78 78
E08 91 99 86 88
Reference transcription versus spoken reference
First, the number of accents and phrase boundaries allocated by the reference tran-scription (of all 10 experts) and the spoken reference (of 3 experts) were compared.
There is no large discrepancy in the number of phrase boundaries in the two references, although the strength of the allocated boundary is not always the same. The number of accents allocated by the spoken reference is somewhat higher than the number of ac-cents allocated by the reference transcription. Table 2.8 shows the confusion matrices for the comparison.
To obtain a more revealing view on the performance, again the accuracy, precision, recall and Fβ-value were computed. Table 2.9 gives these performance measures for phrase boundaries (for both methods described above) and accents. Again, the refer-ence transcription was taken as referrefer-ence and the spoken referrefer-ence was taken as test case.
Table 2.8: Confusion matrices for allocation of phrase boundaries and accents for reference transcription versus spoken reference.
consensus
no weak medium strong
no 303 7
spoken weak 4 7 5
medium 2 11
strong 1 20
consensus accent no accent
spoken accent 106 25
no accent 6 223
Table 2.9: Performance measures for comparison between reference transcription and spoken reference.
accuracy precision recall Fβ=1
bound (method 1) 97 92 87 89
bound (method 2) 95 84 76 80
accent 91 81 95 87
These results show that with respect to phrase boundary allocation, the spoken ref-erence corresponds rather well with the refref-erence transcription when we consider method 1. When we consider method 2, the correspondence is slightly lower, but still rather good. With respect to accent allocation the spoken reference corresponds rather well with the reference transcription.