Quality of Telephone-Based Spoken Dialogue Systems phần 7 potx

270 all relate to the system’s output voice (dimensions intelligibility, friendliness and voice naturalness). The friendliness of the system thus seems to be highly related to its voice. The final dimension ‘clarity of information’ does not form a cluster with any of the other questions. These clusters can now be interpreted in the QoS taxonomy. The ‘personal impression’ cluster is mainly related to comfort, the ‘pleasantness’ question (B24) to user satisfaction as well. Cluster 2 (dialogue smoothness, B19 and B21) forms one aspect of communication efficiency. The global quality aspects covered by questions B0 and B23 (Cluster 3) mainly relate to user satisfaction. The strong influence of the ‘perceived system understanding’ question (B5) on this dimension has already been noted. This question is however located in the speech input/output quality category of the QoS taxonomy. Cluster 4 is related to system behavior (B9, B10 and B11), and can be attributed to dialogue coop- erativity, question B10 also to dialogue symmetry. The questions addressing interaction flexibility (B13 and B14) belong to the dialogue symmetry category. ‘Naturalness’ (B12 and B18) is once again related to both dialogue cooperativ- ity and dialogue symmetry. These two categories cannot be clearly separated with respect to the user questions. Questions B15, B17 and B20 all reflect com- munication efficiency. Cluster 8, related to informativeness (B1, B2 and B4), is attributed to the dialogue cooperativity category. This is not true for Cluster 9 (B6 and B8): Whereas B8 is part of dialogue cooperativity, B6 fits best to the comfort category. Cluster 10 (B7, B16 and B22) is mainly related to the speech output quality category. However, question B16 also reflects the agent personality aspect, and thus the comfort category. The stand-alone question B3 is part of the dialogue cooperativity category. A similar analysis can be used for the judgments on the part C questions of experiment 6.3, namely questions C1 to C18 (the rest of the questions have either free answer possibilities or are related to the user’s expectations about what is important for the system). A hierarchical cluster analysis leads to the dendrogram which is shown in Figure 6.3. Most clusters are related to the higher levels of the QoS taxonomy. The first cluster comprises C1, C9, C12, C13, C14 and C18: These questions are related to user satisfaction (overall impression, C1 and C9), the system’s utility (C12, C13), task efficiency (reliability of task results, C14) and acceptability (C18). The second cluster (C8, C11) relates to the usability and the ease of using the system. Question C8 will also address the meta-communication handling capability. Cluster 3 (C2, C3) reflects the system personality (politeness, clarity of expression). Cluster 4 (C10, C16) is once again related to usability and user satisfaction (ease of use, degree of enjoyment). The fifth cluster captures the system’s interaction capabilities (initiative and guidance; C4 and C7). Cluster 6 describes the system’s task (task success, C5) and meta-communication (C6) capabilities. The final two questions (C15, C17) reflect the added valueprovided Quality of Spoken Dialogue Systems 271 Figure 6.3. Hierarchical cluster analysis of part C question ratings in experiment 6.3. Dendro- gram using average linkage between groups. by the service, and are thus also related to the service efficiency category. Also the part C questions have been associated with the categories of the QoS taxonomy, see Figure 6.1 and Tables 6.5 and 6.6. Similar to the factor analysis, the cluster analysis shows that many questions of part B and part C of the experiment 6.3 questionnaire group into categories which have been previously postulated by the QoS taxonomy. Part B ques- tions can mainly be associated with the lower levels of the taxonomy, up to communication efficiency, comfort and, to some extent, task efficiency. On the other hand, part C questions mostly reflect the higher levels of the taxonomy, namely service efficiency, usability, utility and acceptability. User satisfaction is covered by both part B and part C questions. The relationship shown in Figure 6.1 will be used in Section 6.2.4 to identify subjective ratings which can be associated to specific quality aspects. The results of multidimensional analyses give some indications on the rel- evance of individual quality aspects for the user, in that they show which di- mensions of the perceptual space can be distinguished. The relevance may additionally be investigated by directly asking the users which characteristics of a system they rate as important or not important. This was done in Question 4 (4.1-4.15) of experiment 6.2, and Questions A8 and C22 of experiment 6.3. The data from experiment 6.2, which will be discussed here, have been ranked with respect to the number of ratings in the most positive category and in case of equality to the accumulated positive answers to the statements (two categories close to the “agree” label, and minus the accumulated number 272 Quality of Spoken Dialogue Systems 273 of negative answers (two categories close to the “disagree” label, and The resulting rank order is depicted in Table 6.7. The rank order shows that manner, transparency and relevance, and partly also meta-communication handling and interaction control seem to be of major importance to the users. The result may be partly linked to the particularities of the BoRIS system (repetition capability, modification capability), but the three major aspects – manner, transparency and relevance – will be of general importance for other applications as well. They are all related to the basic communicative and functional capabilities of the system (service aspects have not been addressed by questions 4.1 to 4.15). The highest ranking is observed for the speech input and output capabilities, which is the basic requirement for the interaction with an SDS. The overall system quality seems to be largely af- fected by a relatively low intelligibility of the TTS speech output. Transparency subsumes the transparency of how to use the system, as well as its functional capabilities. This quality aspect seems to reflect whether the user knows what to say to the system at each step in the dialogue, in which format, as well as the system’s navigation (modification, repetition and dialogue continuation) capa- bilities. It may result in discomfort and stress if the system is not transparent enough. Relevance can be defined on an utterance level (relevance of each utterance in the immediate dialogue context) or on a global information (task) level. In the qualitative interview, it turned out that the global information level seems to pose problems with the current BoRIS version, due, in part, to database problems, but also due to the low detail of information provided by the current system version. The user’s background knowledge and the level of experience play a role in the judgement of overall quality. The qualitative interview of experiment 6.2 shows that test subjects who had no specific idea about such a system rated it generally better than persons with a specific idea. In the questionnaire, high expectations resulted mainly in more positive quality judgments after using the system. This could clearly be observed for the judgments of the female test subjects. 6.2.3 Multidimensional Analysis of Interaction Parameters Apart from the users’ quality judgments, also the interaction parameters will be related to each other. Such relations – if they are known – can be used to define meaningful evaluation metrics, and to interpret the influences of individual system components. This section will give a brief overview about relationships which are reported in the literature and present the results of a factor and cluster analysis of the data collected in experiment 6.3. A deeper analysis with respect to the QoS taxonomy follows in the subsequent section. 274 A number of analyses report the obvious relationship between dialogue du- ration DD and turn-related parameters. For example, Polifroni et al. (1992) found out that the overall number of user queries correlates highly with DD The correlation between DD and the number of unanswered user queries was considerably lower The different problem-solving strategies applied in the case of misunderstandings probably have a significant impact on the duration of the interactions. Sikorski and Allen (1997) investi- gated the correlation between dialogue duration and recognition accuracy. The correlation turned out to be unexpectedly low The authors indicate three potential reasons for this finding: A robust parsing strategy, which makes it more important which words are correctly recognized than how many. Misunderstandings, i.e. the system taking an action based on erroneous understanding, seems to be more detrimental to task success than non- understanding, where both the system and the user are aware of the situation. A system which is robust in this respect (i.e. one that tries to form an inter- pretation even when there is low confidence in the input) can create a high variance in the effectiveness of an interaction, and thus in the length of the interaction. A certain amount of nondeterminism (random behavior) in the system im- plementation, which could not be compensated for by the small number of test subjects. Thus, the dialogue strategy may be a determining factor of dialogue duration, although the number of turns remains an important predictor. Several parameters indicate speech input performance on different levels. Gerbino et al. (1993) compared absolute figures for correctly understood sen- tences in a field test (30.4% correct, 21.3% failed, 39.7% incorrect) to the ones in a laboratory situation (72.2% correct, 11.3% failed, 16.5% incorrect). Obvi- ously, the field test situation was considerably more difficult for the recognizer than a laboratory situation. For the field test situation, the figures can be com- pared to the recognition accuracy (SA = 14.0%, WA = 52.4%). It turns out that the understanding error rate is approximately in the middle of the word and sentence error rates. The relation between ASR performance (WA) and speech understanding performance (CA) was also investigated by Boros et al. (1996). Both mea- sures can differ considerably, because WA does not make a difference between functional words and filler words. Thus, perfect CA can be reached without perfect WA. On the other hand, CA may become lower than WA when words which are relevant for understanding are missing in the system’s interpretation. Results from a test corpus recorded over the public telephone network how- Quality of Spoken Dialogue Systems 275 ever showed that WA and CA have a strong correlation, resulting in a nearly linear relationship between WA and CA. For the tested system, WA seems to be a good predictor for CA, as speech recognizer and parser collaborate smoothly. In general, it cannot however be guaranteed that an increase in ASR performance will always lead to better speech understanding capabilities. If new words are added to the ASR vocabulary, this could provoke a degradation of speech understanding performance. Investigations carried out at MIT (Po- lifroni et al., 1998) however showed that a decrease in word error (from 21.7% to 16.4%) also resulted in a decrease of sentence error (42.5% to 34.3%) and in speech understanding error (31.7% to 23.8%). All in all, relatively strong correlations between the ASR and speech understanding performance measures can be observed. Speech recognition and speech understanding performance will also be re- lated to task success. Rosset et al. (1999) illustrate the relationship between word error rate and task success for two system configurations which differ in terms of a rejection threshold for poorly recognized words. Implementation of such a threshold resulted in an increasing task success rate, especially for high word error rates. Transaction success is however not necessarily closely linked to speech understanding performance. Gerbino et al. (1993) report that their system had a task success rate of 79% with only 30.4% correctly understood sentences. Better predictors of task success may be found in the system-answer- related parameters. Goodine et al. (1992) compared the percentage of correctly resolved scenarios (as a measure of task success), the AN:CO parameter, and It turned out that AN:CO was a good indicator of task success, but that the parameter over-penalizes incorrect system answers. During experiment 6.3, a more-or-less complete set of interaction parameters was collected. On this set, a factor analysis has been carried out, in the same way as was done for the quality judgments (principal component analysis with Varimax rotation and Kaiser normalization, missing values were replaced by means). The complete results will not be reproduced here due to space limi- tations; only a short summary will be given. 10 factors were extracted which accounted for 81.9% of the variance in the parameter data. Factor 1 loads high on all speech-input related parameters (IC, UA, WER, WA, as well as on the parsing parameters (PA:CO and PA:FA) and on Apparently, this factor is related to the speech input capabilities. Factor 2 loads high on the duration-related parameters DD, STD, SRD, # TURNS, WPST and WPUT, and seems to be related to communication efficiency (additional loading on PA:PA ) . Factor 3 seems to be related to the system’s meta-communication capabilities. It loads high on SCR, UCR, CA:AP, CA:IA, IR and PA:FA. Factor 4 is related to the system’s answer capability. It has very high load- ings on AN:CO, AN:FA, and Factor 5 reflects task 276 success: Loadings are high for and Interestingly, the per- configuration version of does not show a high loading. Apparently, the sys- tem configuration plays a significant role for determining task success. Factor 6 might be explained by the cognitive demand put on the user. It only shows high loadings on UTD and URD. The last four factors are difficult to interpret. They only show high loadings on one or two interaction parameters which are not obviously related. Figure 6.4. Hierarchical cluster analysis of interaction parameters in experiment 6.3. Dendro- gram using average linkage between groups. Links between interaction parameters can additionally be addressed by a hi- erarchical cluster analysis, as was performed for the subjective judgments. The resulting dendrogram is shown in Figure 6.4. The first cluster contains three pa- Quality of Spoken Dialogue Systems 277 rameters which are all related to meta-communication (system error messages, partially correct answers, and the DARPA error). The next cluster contains two parameters related to communication efficiency ( DD and # T URNS ). The third cluster relates once again to meta-communication, in particular to the correc- tion capabilities (correction rates, inappropriate system utterances, and failed speech understanding). Cluster 4 contains 6 parameters related to speech recog- nition, and thus to the speech input quality of the system. The # B ARGE -I NS parameter seems to be independent of all other parameters. The following cluster consists of 7 parameters which all seem to be related to communication efficiency: STD, SRD, WPUT, WPST and # U SER Q UES - TIONS all carry a direct impact on the dialogue length, and PA:PA and AN:FA willalso contribute to lengthening of the dialogue due to subsequent clarification dialogues. The next cluster is somehow related to task efficiency. It contains the two task success measures and and two parameters which reflect the number of correct system answers ( AN:CO and The following two parameters (URD and UTD) do not form a cluster in a proper sense. They reflect the characteristics of the user, but cannot be interpreted with respect to their quality impact. The next 8 parameters all relate to speech input quality: The first group of three parameters addresses ASR performance, and the second group of five parameters addresses speech understanding perfor- mance. It is interesting to note that the parameter forms a cluster with the word accuracy measures. This is an indication that the recognition rate seems to play an important role for task success, and that task success (as expressed by the coefficient) will depend on the target recognition rate of the system configuration under test. In the group of speech-understanding-related param- eters, the CA:AP parameter has to be noted. Apparently, appropriate system answers are related to the system’s speech understanding capability. The final two parameters do not form any specific cluster. In particular, no clustering of with the other task-success-related parameters can be observed. Both cluster and factor analysis show that interaction parameters mostly ad- dress the lower level categories of the QoS taxonomy, namely speech input quality, dialogue cooperativity, communication efficiency, task efficiency, and comfort. This finding has to be placed in contrast to the higher level cate- gories reflected in the dimensions of the user judgments, e.g. usability, service efficiency, user satisfaction and acceptability. Although individual questions (mainly part B questions) can be attributed to the lower level categories, the more wholistic user view of the service, discussed in Chapter 3, is confirmed here. The finding may have some implications for the construction of prediction models for SDS-based services: If interaction parameters mainly address low- level categories and the user judges in high-level categories, then it might be 278 difficult to predict global quality aspects perceived by the user from interaction parameters. Kamm et al. (1997a) already noted relatively weak correlations between users’ perceptions of system quality and system performance metrics. It may be an indication that global quality aspects are not the right target to be predicted from interaction parameters, but that individual quality aspects are more adequate for this purpose. The idea will be further discussed in Section 6.3. 6.2.4 Analysis of the QoS Schematic The factor and cluster analyses described in the previous two sections high- light the relationships amongst subjective quality judgments or interaction pa- rameters. The extracted factors have been interpreted in the light of the QoS taxonomy introduced in Section 2.3.1, however without giving further justifica- tion for the classification it defines. In this section, the individual categories of the taxonomy will be initially addressed in isolation, showing the correlations between subjective judgments and interaction parameters. The findings will then be interpreted with respect to the prediction potential for global quality aspects like the ones addressed by questions B0 or C18. A correlation analysis for the individual categories of the QoS taxonomy is described in the following discussion. As most of the parameters and subjective judgments do not show a gaussian distribution when accumulated over all sys- tem configurations, Spearman rank order correlation coefficients have been chosen. The correlation tables contain all parameters and questions which have been attributed to a specific category (see Tables 6.5,6.6, and Figure 6.1 for the subjective ratings, and Tables 3.1 and 3.2 for interaction parameters), as well as all additional parameters which show a correlation with one of the associated questions. Correlations which are significant are given in italics. Quality of Spoken Dialogue Systems 279 Informativeness: The relevant questions and parameters are listed in Table 6.8. High correla- tions are observed between questions Bl and B2, as well as between and AN:CO. Apparently, the accuracy and the completeness of the provided information are not easy to distinguish for the test subjects. Corre- lation between questions and parameters is very low, with the exception of B1 which moderately correlates with and AN:CO. These parameters are however only calculated for 18 dialogues, and the correlations should be interpreted with care. # U SER QUESTIONS is not correlated with any other question or parameter of the list. This corresponds to the wizard’s observation that most users were asking questions in order to assess the system functionality, and not with respect to the restaurant information provided by the system. Truth and evidence: Four questions and five parameters are related to this aspect, see Table 6.9. All questions correlate moderately However, only question B11 also shows some (moderate) correlation to the relevant parameters. The generally low correlations may be an indication that the perception of truth by the test subjects does not necessarily require system answers to be correct from an external point of view. In fact, the test subjects have no possibility to verify the correctness of information provided by the system, except when the system gives explicit feedback on misunderstood items. The high correlations between and AN:CO have already been noted. Also AN:FA shows high correlations to these parameters. Relevance: Relevance is an aspect which is only indirectly covered in the user judg- ments, namely via questions related to perceived system understanding (B5), [...]... (alternative interface), and close-to-significant contributions from C8 (ease of use), C13 (perceived helpfulness) and C14 (perceived reliability) The interpretation of these findings is difficult, but the informative and functional value of the system seems to be important for the test subjects Quality of Spoken Dialogue Systems 2 97 Summary of the analyses: In this section, the relationships between subjective... observed in the other analyses, the # BARGE-INS parameter does not correlate with any of the other entities Quality of Spoken Dialogue Systems 289 Conciseness: The dialogue conciseness is addressed by questions B 17 and B20, as well as by four interaction parameters Only B20 is moderately correlated to DD and # TURNS, but B 17 does not show any high correlation to the interaction parameters This result is... (1997a), with the correlation coefficients in the same order of magnitude Quality of Spoken Dialogue Systems 2 87 There are however strong correlations between the interaction parameters Very close relationships are found between WA, WER, and both for the continuous as well as for the isolated ASR measures The relationships between the corresponding continuous and isolated measures are in the area of. .. Speech input quality, in turn, is linked to communication efficiency, comfort, and task efficiency It can therefore be assumed that speech input performance will not only be reflected in interaction parameters and judgments Quality of Spoken Dialogue Systems 299 directly related to the speech input quality category, but also in those of the dependant categories Several authors address the impact of speech... short user utterances), or to the regular order of scenarios used for the interactions Other judgments on the perceived Quality of Spoken Dialogue Systems 301 Figure 6.10 Comparison of observed and target recognition rates for the recognizer simulation in experiment 6.3 system understanding (Q1), the intelligibility of the system voice (Q2), the impression of having understood what the system required... were observed for Q2 Q3 Q4 Q6 and Q7 see Figure 6.12 Appar- Quality of Spoken Dialogue Systems 305 Figure 6.13 Effect of speech output configurations on subjective ratings on part B questions of experiment 6.3 B0: “Overall impression”; B3: “The information was clear/unclear”; B6: “You had to concentrate in order to understand what the system expected from you?”; B7: “How well was the system acoustically... between five questions (B8, B10, B 17, B19 and C2) and two parameters (# TURNS, WPST) related to the manner of expression Both interaction parameters highly correlate, but they only show weak to moderate correlations to the questions Question C2 does not show any correlation with the part B questions A factor analysis of all questions and Quality of Spoken Dialogue Systems 281 parameters related to manner... situation Users do not feel in a realistic situation and cannot verify the given information It is however astonishing Quality of Spoken Dialogue Systems 283 Figure 6.5 Univariate analysis of covariance for dialogue cooperativity Covariate factors are part B and C questions that none of the manner-related questions shows a significant contribution to cooperativity It may be the case that it is difficult... than 0.5 to one of these questions A moderate correlation between C11 and C13 can be observed A service provided by the system seems to be more helpful when the user is informed about its functionality Quality of Spoken Dialogue Systems 293 Service efficiency: This category comprises the aspects of service adequacy and added value It is addressed by the questions C12, C13, C15 and C 17, from which C12... performance degradation increases the need for (correction) meta-communication, and thus also affects dialogue smoothness Interestingly, the cooperativity of system answers seems to be strongly affected by the ASR performance (by definition, the CA measures are a direct indicator Quality of Spoken Dialogue Systems 303 of cooperativity) Task success also depends on ASR performance: Although the and parameters . accumulated number 272 Quality of Spoken Dialogue Systems 273 of negative answers (two categories close to the “disagree” label, and The resulting rank order is depicted in Table 6 .7. The rank order. astonishing Quality of Spoken Dialogue Systems 283 Figure 6.5. Univariate analysis of covariance for dialogue cooperativity. Covariate factors are part B and C questions. that none of the manner-related. agreement with the one made by Kamm et al. (1997a), with the correlation coefficients in the same order of magnitude. Quality of Spoken Dialogue Systems 2 87 There are however strong correlations between