Can the acoustic analysis of expressive prosody discriminate schizophrenia?
The difficulty to identify the emotional state of others in facial expression (Kohler, Walker, Martin, Healey, & Moberg, 2010) and in the tone of their voice (Hoekert, Kahn, Pijnenborg, & Aleman, 2007) has been well established as one of the main predictors of deterioration in all phases of the disorder: from the first episode (Horan et al., 2012), during its chronicity (Green et al., 2012), and even in its high-risk states (Allot et al., 2014).
These difficulties are part of the alterations in social cognition (described as the ability to construct representations of the relationship between oneself and others, and to use them flexibly in behavior regulation, Adolphs, 2001), along with social perception social, theory of mind, social knowledge and attributional style, which have been consistently linked to schizophrenia (Couture, Penn, & Roberts, 2006; Green & Horan, 2010; Kring & Ellis, 2013; Penn, Sanna, & Roberts, 2008).
Similarly, the emotional expressive ability is also impaired (Cohen, Kim, & Najolia, 2013), although there is evidence (Kring & Moran, 2008) indicating the dissociation between expressiveness and emotional experience, since flat affect does not necessarily lead to a reduction in emotional experience.
Prosody has been studied to a lesser extent than the ability to identify and express emotions facially. In speech, not only the changes in the melody produced by variations in the frequency of opening and closing of the vocal cords are perceived, but also the changes of rhythm, speed, intonation, pauses, intensity and other spectral alterations that are perceived by the listener as melodic variations, and interpreted subjectively as paralinguistic signals, essential for the understanding and interpretation of the utterance and the identification of the emotional and motivational state of the speaker (Patel, 2008).
Receptive prosody studies have shown that patients exposed to voice samples with different emotions and questioned about the emotion they have heard show a marked difficulty to identify such emotions. An extensive meta-analysis (Hoekert et al., 2007) comprising twenty articles with a total of 663 patients, has described a significant effect size (d = -1.24) when comparing the performance of schizophrenic participants and controls.
Regarding expressive prosody, the studies that have collected voice samples and have asked participants to encode different emotional states have concluded that there is a marked difficulty in expressing emotions verbally (Putnam & Kring, 2007). Hoekert et al.'s (2007) meta-analysis has also concluded there was a significant effect size (d = -1.11) between the eleven studies reviewed with data from 186 patients.
Most of the studies on expressive prosody have used tasks in which participants were asked to encode various emotional states perceived in voice (Hoekert et al., 2007); fewer studies have asked their participants to spontaneously narrate sad, happy and anger events they have experienced (Alpert, Rosenberg, Pouget, & Shaw, 2000; Shaw et al., 1999). These methods have, in our view, reduced the ecological validity of the study of expressive prosody, since high intensity discrete emotional states (anger, sadness, fear, etc.) are rarely expressed in colloquial language in the manner described in these studies. In everyday social interaction, the affective state, intentions and aptitude are constantly and inexorably present in the tone of voice, though less explicitly. An alternative method is used by Cohen et al. (Cohen, Iglesias, & Minor, 2009; Cohen & Hong, 2011), consisting of analyzing the prosody of verbal responses triggered by emotional stimuli presentations, using the "International Affective Picture System" (Lang, Bradley, & Cuthbert, 2005.
In contrast to these methods, only three studies have used emotionally neutral readings (Cohen, Alpert, Nienow, Dinzeo, & Docherty, 2008; Dickey et al., 2012; Leentjens, Wielaert, Harskamp, & Wilmink, 1998). To evaluate the non-emotional expressive prosody in schizophrenia, this paper presents a potentially useful technique, using an emotionally neutral text that has been proven useful in quantifying prosody in other disorders (Martinez-Sanchez, Meilan, Perez, Carro, & Arana, 2012). The procedure consists of the semiautomatic analysis of the variations in the trajectory of pitch and height perception of the fundamental frequency (of the vocal cords' vibrations) of the vocalic syllable nuclei, as this is the point of greatest loudness, using a purely acoustic base to extract the harmonic peak without the need of phonetic segmentation. The data derived from the behavior of the F0 in the intensity of vocalic segments yield a complete melodic pattern of the speaker that shows significant changes in tone, both upstream (prosodic peaks) and downstream (prosodic valleys), within the syllabic nucleus, as well as between different nuclei (see Annex I for a description of the prosodic parameters used).
This procedure has many advantages over previously used prosodic analyses, since it increases the reliability and validity of results, speeds up the production of prosodic parameters and minimizes the influence of the coding skills of the subject, as it uses an emotionally neutral text. It also increases its ecological validity, as it is closer to the colloquial language used in everyday interactions. Finally, the procedure does not require phonetic segmentation, which virtually eliminates any errors the experimenter could commit in the process of quantification, as well as any differences in estimation between various experimenters.
In the present work, this procedure is used in order to objectively quantify the deficits in expressive prosody in schizophrenia, as well as to assess its discriminatory power between groups. It is hypothesized that the group of schizophrenia patients will show a significantly flatter prosodic profile, characterized by less variability in the dynamics and the path of the vocalic nuclei and in voice intensity as well as an increase in the number of pauses than those obtained by the control group.
A cross-sectional, analytical, observational and retrospective design was used.
A sample of 80 participants, divided into two groups, was recruited: 45 patients diagnosed with schizophrenia and 35 asymptomatic controls.
The group of patients ([M.sub.age] = 39.49, SD = 10.89; 71.1% male) were recruited from various Mental Health units of the Andalusian Health Service of the province of Jaen (Therapeutic and Rehabilitation Community of the Jaen area and the Mental Health Clinics of Martos and Andujar). All participants were evaluated using the clinical version of the Structured Clinical Interview (First, Spitzer, Gibbon, & Williams, 1996), following the criteria established in the DSM-IV-TR (2000). The average duration of the disorder, from its initial diagnosis, was 21.17 years (SD = 5.65), the mean number of relapses was 3.47 (SD = 1.89) and the mean time elapsed since the last relapse was 45.88 days (SD = 25.67). The average dose of chlorpromazine equivalent units was 669.88 mg / day (SD = 559.31).
Meanwhile, the control group ([M.sub.age] = 35.34, SD = 10.48; 62.9% male) was matched with the patient sample for age, sex and educational level, and was extracted from the same social environment as the patient sample. They had no history of mental or neurological disorders or drug or alcohol abuse, which were considered exclusion criteria.
Materials and Procedure
Brief Psychiatric Rating Scale (BPRS; Lukoff, Nuechterlein, & Ventura, 1986) in the Spanish validation by Peralta and Cuesta (1994). The 0-5 points response range was used as it increases the inter-rater reliability (Bech, Larsen, & Andersen, 1988). It is composed of 18 items, to be administered by an experienced therapist after a semi-structured interview (15-25 minutes in length). Each item is scored using a Likert-type scale with 5 levels of intensity, where 0 represents the absence of the symptom and 4 represents extreme gravity.
In order to record speech, a professional Fostex FR-2LE recorder was used, with a resolution of 24 bits and a 48 kHz sampling rate, using a cardioid AKG D3700S microphone. Samples were edited using the acoustic voice analysis 5.1.42 Praat program (Boersma & Weenink, 2013). Annex I contains the definition of the various parameters used.
The study was conducted between June and December of 2013. All participants were adequately informed in order to sign their consent according to the protocols of the Bioethics Committees of the participating institutions. This study complied with the ethical principles of the Declaration of Helsinki for medical research involving human subjects. The procedure was performed in a single session, initially collecting socio-demographic information (age, marital status, etc.) and clinical data (age at onset of symptoms and diagnosis, duration of illness, number of admissions, time since last admission, etc.) as well as administering the BPRS. The doses of chlorpromazine equivalent units ingested by patients were also registered. Subsequently, speech recordings were made.
The task entailed reading the first paragraph (405 syllables) of "Don Quijote" by Miguel de Cervantes. The recordings were performed in a silent room (but not acoustically isolated), placing the microphone 8 cm away and at a 45[degrees] angle from the participant's mouth in order to prevent any aerodynamic noise.
To quantify prosodic patterns, the automatic prosodic transcription of the recordings was performed using the algorithms implemented by Mertens (2004) on the Praat program (Boersma & Weenink, 2013). The estimation of the prosodic speech profile was performed analyzing the variations in the height trajectory and pitch perception (prosodic peaks and valleys) of the F0 of the vocalic syllable nuclei that contain voice signals, on a peak intensity delimited -3dB and -9dB to left and right, respectively, in order to represent the melodic movements perceived by the human ear. The value of the left limit (-3dB) eliminates most of the microprosodic disturbances and stylizes the beginning of the syllable, while the right (-9dB) limit preserves the variations in tone of accented vowels.
In this paper, a detection range for the F0 of 65-650 Hz was established for 0.005s windows; the following threshold intensity was established for the automatic segmentation in the stylization of the algorithm: Glissando = 0.32/[T.sup.2], DG = 30, dmin = 0.05. To determine the presence of a vowel, a 0.32/[T.sup.2] semitone threshold was allocated, where T is the duration of the vowel in seconds. If the tone's exchange rate is higher than the threshold defined by the perceptual values of voice detection, a value proportional to the glissando threshold (continuous slippage of the melodic line in the same syllable) was assigned, whereas if the value is lower than the threshold, the same value as the median of the voice sample analyzed was assigned. It should be noted that while the standard psychoacoustic threshold for isolated vowels is G = 0.16/[T.sup.2], voice flow is rarely linear during natural speech, hence, the value assigned in this present study has been shown to more adequately model voice variations, especially in automatic transcription.
The IBM SPSS (version 21) statistical package was used. The Student-f test for independent samples was used to define the differential prosodic profile between groups and Pearson correlations were used to assess the relationship between variables. Finally, a discriminant analysis was performed in order to assess the ability of prosodic variables to classify subjects into both groups.
Different statistical tests were conducted to assess the absence of statistically significant intergroup differences among the sociodemographic variables. No differences for the variable "age" ([t.sub.79] = -1.76; p = .091), educational level (measured in months of schooling; [t.sub.79] = 1.74; p = .085), or for the distribution of sex per group ([chi square] = .611; p = .434) were observed.
The mean comparisons show the existence of significant intergroup differences in all the prosodic variables, but not for those dependent on the frequency (F0) (Table 1). The main prosodic parameters (valleys, prosodic dynamics, inter- and intra-syllabic and phonation trajectories) of the Schizophrenia patient group yielded significantly lower levels than those obtained by the control group (Figure 1), showing in general, a sparsely prosodic and melodically flatter speech than that of controls (Figure 2). Moreover, they spent more time performing the task, made a greater proportion of pauses and exhibited a significantly lower voice intensity, accentuating their perception of dysprosody.
As expected, education level significantly affected the performance of the task in the control group. For example, in the schizophrenia group, the higher the educational level, the lower the time spent on reading the text and the lower the proportion of pauses performed. It should be noted that for a test to be used for screening, it must be scarcely sensitive to the effects of education in the pathological group; the poor correlation between prosodic variables and educational level in the patient group for this task can be appreciated in Table 2.
The years of chronicity of the disorder is the clinical variable most strongly associated with prosodic variables in the schizophrenia patients group; the greater the number of years elapsed since diagnosis of the disorder, the smaller the intrasyllabic (r = -.377; p = .028), and phonation (r = -.422; p = .013) trajectories were. Similarly, the time elapsed since the last relapse correlated with the phonation trajectory (r = .404; p = .018). Drug treatment did not induce significant changes in prosody, although a trend was observed which suggests that the higher the dose of chlorpromazine was, the less time was spent on the reading task (r = -.280; p = .063) and fewer pauses were made (r = -.251; p = .091). Moreover, the BPRS scores were not significantly correlated to the prosodic variables, although the positive symptoms scale score negatively correlated with the intensity of voice (r = -.346; p = .021). The scale's item that evaluates blunted affect did not correlate significantly with any prosodic parameter, obtaining the highest degree of correlation with the "Intrasyllabic trajectory" variable and with blunted affect (-.219; p = .118). No significant correlations were found between the total scores of the BPRS scale and the prosodic parameters evaluated, or between these parameters and the scores obtained in the positive and negative symptoms subscales.
Finally, a discriminant analysis was performed, in order to assess the ability of the prosodic parameters studied to distinguish subjects in both groups. The canonical discriminant function explained 100% of the variance (canonical correlation = .828; [[lambda].sub.14] = .314; [[chi square].sub.14] = 82.16; p < .001), with Intensity, Intrasyllabic trajectory,
Total length, Phonation trajectory and Pause rate being the variables with the greatest discriminating power (with standardized canonical discriminant function coefficients of .630, .541, -.403, .365 y -.344 respectively). Conversely, the dependent variables of the fundamental frequency (M [F.sub.0] = -.003; [F.sub.0] Range = -.026; [F.sub.0] SD = .077) were those that showed less discriminatory power. Overall, the discriminant analysis allowed for the correct classification of 93.8% of the original data (Table 3); in the cross-validation analysis, 87.5% of cases were correctly classified.
The objective of this research was to study the differential expressive prosodic patterns in a group of schizophrenic patients and in a group of asymptomatic subjects using an unemotional text as the reading task. The results show the existence of marked intergroup differences, with the clinical group exhibiting a slow and low-intensity speech, with many pauses. Moreover, this group's dysprosody is characterized by scarce tone changes, both within the syllable nucleus, and between adjacent syllabic nuclei. All these characteristics yield an "emotionally flattened" speech (Tremeau, 2006).
These results concur with those obtained in several studies. Firstly, the low dynamics of both the inter- and intra-syllabic nuclei observed in our results, in spite of not have been studied before, is the result of the scarce changes in F0. This is consistent with previous studies (Alpert et al., 2000) reporting that the schizophrenic speech is characterized by few inflections of speech. Secondly, the large number of pauses has also been previously identified as a characteristic of the disease (Alpert, Kotsaftis, & Pouget, 1997; Clemmer, 1980; Cohen & Elvevag, 2014; Cohen, Mitchell, & Elvevag, 2014). They can be attributed to the reduction in verbal fluency and the discrimination of phonemes, processes that are altered in schizophrenia (Johnson-Selfridge & Zalewski, 2001; Kugler & Caudrey, 1983). Thirdly, the voice signal is less intense (Pascual, Sole, Castillon, Abadia, & Tejedor, 2005), which also accentuates the perception of dysprosodic speech. Finally, these subjects take longer to complete the task, evidencing the limited cognitive resources available to perform a complex task such as reading (Cohen, McGovern, Dinzeo, & Covington, 2014; Melinder & Barch, 2003).
The dependent parameters of the fundamental frequency (mean, standard deviation and range) showed no differences between groups. This is not unexpected, as even though these measures contribute to variations in tone, they are microprosodic measures, as opposed to the suprasegmental parameters studied, which have been shown to be significant intergroup discriminators.
The observed differences in the variations in the syllabic dynamics of the vocalic nucleus are especially noteworthy, as they show a differential prosodic profile in both groups regarding their melodic slopes. Thus, although the percentage of upward vocalic nuclei (expressed by prosodic peaks) did not differ between the groups, the downward vocalic nuclei (prosodic valleys) were significantly lower in the group with schizophrenia. In the Spanish language, declarative intonation is typically downward, fully explaining the abovementioned, while the interrogative intonation is, however, generally upward, presenting an incomplete utterance (Cantero, 2003). As the text used in the task was a declarative text, the clinical group's intonation was clearly inadequate, a fact that the listener may perceived as discordant with the meaning of the sentence being read.
The clinical variables have proven to be insignificant in expressive prosody. Our results show a correlation between the years of chronicity of the disorder and the impairment of intersyllabic and phonation trajectories. Although there is a lack of data from other investigations to compare, it is known that the ability to identify facial and prosodic expressions reduces with the duration of illness (Kucharska-Pietura, David, Masiak, & Phillips, 2005; Silver, Goodman, Knoll, Isakov, & Modai, 2005; Silver, Shlomo, Turner, & Gur, 2002). Although drug treatment does not induce changes in prosody, beyond increasing the duration of the task and the number of pauses, Hoekert et al.'s (2007) meta-analysis does not verify the existence of any significant relationships between these variables.
The relationship between the increase in positive symptoms and the reduction in the intensity of the voice is paradoxical, because one would expect the negative symptoms to be related to a lower intensity of voice (considering it an indicator of flat prosody), as reported by Cohen and Hong (2011), although their results did not reach statistical significance, while positive symptoms would correlate to an increase in verbal fluency. It is known that positive symptoms correlate with the difficulty in discriminating the fundamental frequency changes in sentences with an emotional content (Matsumoto et al., 2006). Similarly, patients who experience hallucinations and delusions show alterations when identifying changes in the tone of voice (Johns et al., 2001).
Finally, the scores of the BPRS subscales have not yielded any significant correlations with prosody. These scales' limitations are well-known (Cohen & Elvevag, 2014; Nicholson, Chapman, & Neufeld, 1995). Even though they are able to identify differences of up to six standard deviations when comparing negative symptoms of patients and controls (Emmerson et al., 2009), they are relatively insensitive to changes in the patient's condition and they induce response biases that make it difficult for even trained evaluators to notice specific aspects of behavior related to alogia and blunted affect within the patient's speech (Alpert, Shaw, Pouget, & Lim, 2002). On the other hand, our results coincide with those reported by Cohen et al. (2013), who found no relationship between the symptoms of a schizophrenic group and variables related to the expressive prosody (number of pauses and variability of F0).
While the slow implementation of the task (duration and rate of pauses) is conditioned by educational level in both groups (though to a greater extent in the clinical group), the variables related to the trajectory of the syllabic nuclei exhibit no relationship to these variables in any of the two groups, showing that the prosody results are scarcely sensitive to the cultural level, coinciding with the results reported by Leentjens et al. (1998).
The used procedure has proven to be, in our view, a valid and reliable alternative to accurately record nonemotional expressive prosody in colloquial speech, as its results can relate to patients' everyday interactions with their environment. The small number of existing studies using non-emotional stimuli to assess the expressive prosody in schizophrenia is surprising (Cohen et al., 2008; Dickey et al., 2012; Leentjens et al., 1998).
Furthermore, the procedure provides numerous advantages to those traditionally employed. It allows to objectively quantify the degree of dysprosody in a fast and non-intrusive way, without causing discomfort to the patient. Additionally, the acoustic analyses are highly sensitive to changes in the voice; therefore, their use is potentially useful in the study of the evolution of the disorder and in evaluating drug treatments. The high discriminating ability (93.8%) is higher than that achieved by Kliper et al. (80.95%; Kliper, Vaizman, Weinshall, & Portuguese, 2010).
This study has several limitations. Firstly, patients were medicated, making it impossible to assess the differential effect of drug treatment on prosody; however, the correlations obtained seem to rule out any connection. Secondly, although several variables that can modulate the results (age, education level, etc.) have been evaluated, it is necessary to expand the number of variables that can influence the results. Thirdly, the possible existence of differences in prosody dependent on the demand of the task should be researched, since the cognitive demand required while reading a neutral text is very different from that required by speech in response to stimuli of an emotional character. The differences in the demands imposed by the tasks can potentially explain the disparity in the results obtained in various investigations, since cognitive resources particularly determine speech production in patients with schizophrenia.
Future studies, with an extended sample, may shed more data on the utility of this procedure and its clinical implications, especially in longitudinal studies. The obtained results support the use of the procedure; however the refinement of acoustic analysis algorithms should be sought, in order to achieve higher levels of discriminatory power between groups.
Annex I. Definition of the used parameters Measure Description Duration Total duration of the reading task, including pauses (s.) Intensity Voice intensity (dB) % Pauses Rate -Percentage of inter-syllabic pauses with a duration > 300ms relative to the total time of phonation (%) M [F.sub.0] Mean value of the Fundamental frequency ([F.sub.0]) (Hz) [F.sub.0] SD Standard Deviation of the [F.sub.0] (Hz) [F.sub.0] Range Syllabic nuclei amplitude (2%-98%) (ST) Syllabic dynamics Percentage of syllables with important changes in tone [abs (changes) > 4ST] (%) Prosodic peaks Percentage of syllables with upward tone changes ([greater than or equal to] 4ST) (%) Prosodic valleys Percentage of syllables with downward tone changes ([less than or equal to]-4ST) (%) Intra-syllabic Trajectory Tone changes (abs. values) of the syllabic nuclei / duration (ST/s) Inter-syllabic Trajectory Tone changes (abs. values) of the syllabic nuclei, with the exception of pauses / duration (ST/s) Phonation Trajectory Sum of the inter- and intra-syllabic / phonation time (ST/s)
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Francisco Martinez-Sanchez (1), Jose Antonio Muela-Martinez (2), Pedro Cortes-Soto (2), Juan Jose Garcia Meilan (3), Juan Antonio Vera Ferrandiz (1), Amaro Egea Caparros (1) and Isabel Maria Pujante Valverde (1)
(1) Universidad de Murcia (Spain)
(2) Universidad de Jaen (Spain)
(3) Universidad de Salamanca (Spain)
Correspondence concerning this article should be addressed to Francisco Martinez-Sanchez. Dpto. de Psicologia Basica y Metodologia. Facultad de Psicologia. Universidad de Murcia. Campus Universitario de Espinardo. 30100. Murcia (Spain).
Table 1. Descriptive statistics and mean comparison tests of the analyzed prosodic parameters Group VV. DD. Control (SD) Schizophr. (SD) Task duration (s) 83.85 (15.65) 124.12 (43.15) Intensity (dB)0 73.79 (1.40) 71.08 (1.50) Pause rate [greater than or 28.58 (5.98) 37.64 (10.69) equal to] 300 ms (%) Mean [F.sub.0] (Hz) 156.57 (43.89) 143.02 (40.33) [F.sub.0] SD (Hz) 29.67 (8.32) 27.50 (10.36) [F.sub.0] Range (ST) 11.00 (2.90) 11.27 (3.84) Syllabic dynamics (%) 4.31 (3.02) 2.80 (2.50) Prosodic peaks (%) 1.09 (1.10) .76 (1.19) Prosodic valleys (%) 3.23 (2.22) 2.05 (1.85) Intra-syllabic Trajectory (ST/s) 18.51 (2.87) 14.11 (2.68) Inter- syllabic Trajectory (ST/s) 24.26 (4.89) 20.86 (7.20) Phonation Trajectory (ST/s) 21.32 (3.54) 17.26 (3.97) VV. DD. t P Task duration (s) -5.25 .001 Intensity (dB)0 8.22 .001 Pause rate [greater than or -4.49 .001 equal to] 300 ms (%) Mean [F.sub.0] (Hz) 1.43 .156 [F.sub.0] SD (Hz) 1.00 .303 [F.sub.0] Range (ST) -3.43 .733 Syllabic dynamics (%) 2.44 .017 Prosodic peaks (%) 1.26 .206 Prosodic valleys (%) 2.59 .011 Intra-syllabic Trajectory (ST/s) 7.05 .001 Inter- syllabic Trajectory (ST/s) 2.39 .014 Phonation Trajectory (ST/s) 4.75 .001 Note: dB = Decibels; Hz = Hertz; [F.sub.0] = Fundamental frequency; ST/s= Semitones/second. Table 2. Correlations between prosodic variables and months of schooling Control Schizophrenia Education Education Task duration -.506+ -.353 % Pauses -.567+ -.305 Syllabic dynamics -.475+ -.323 Prosodic peaks -.561+ -.289 Prosodic valleys -.371 -.259 Intra-syllabic Trajectory -.083 -.208 Inter-syllabic Trajectory -.173 .027 Phonation Trajectory -.134 -.019 Note: + p < .006 (Bonferroni's correction). Table 3. Percentage of correctly classified cases in the discriminant analysis Prognosticated group membership Control Schizophrenia Control 32 (91.4%) 3 (8.6%) Schizophrenia 2 (4.4%) 43 (95.6%) Note: 93.8% of the original cases were classified correctly.
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|Title Annotation:||texto en ingles|
|Author:||Martinez-Sanchez, Francisco; Muela-Martinez, Jose Antonio; Cortes-Soto, Pedro; Garcia Meilan, Juan J|
|Publication:||Spanish Journal of Psychology|
|Date:||Jan 1, 2015|
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