Autism Spectrum Disorder (ASD) encompasses a heterogeneous group of neurodevelopmental disorders typically manifesting during the preschool years and characterized by persistent deficits in social interaction and communication, associated with restricted, repetitive patterns of behavior, interests and/or activities (American Psychiatric Association – APA, 2013).

The term ‘Autism Spectrum Disorder’ was introduced in the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) (APA, 2013), serving as an ‘umbrella term’ to describe a continuum of clinical conditions with varying degrees of severity and overlapping boundaries (Vivanti, 2021). The spectrum ranges from individuals with ‘Low-Functioning Autism’ (LFA), who require very substantial support throughout life due to severe symptoms and associated intellectual disability (ID), to individuals with ‘High-Functioning Autism’ (HFA), who, while needing support, are able to live autonomously and possess normal or above-average intelligence quotient (IQ) levels (APA, 2013; Krüger et al., 2018).

The intraindividual variability that characterizes ASD in terms of etiopathogenesis and symptomatology extends to the linguistic and communication dimensions as well. At one end of the spectrum, there are individuals who have never acquired verbal language and do not respond to or initiate any communicative exchanges (Vivanti, 2021: 103). At the other end, there are those who, despite acquiring language at a typical pace or with relatively minor delays (APA, 2013; Kim et al., 2014), may still exhibit impairments in their overall communicative competence.

Since the earliest descriptions of autism (e.g. Kanner, 1943), it has been observed that individuals with ASD struggle with pragmatic aspects of language and with producing acoustically appropriate speech (Maes et al., 2023). These challenges often result in distinctive speech patterns, particularly marked by disordered prosody. For example, individuals with ASD ‘may speak too softly, too loudly, too emotionlessly, or too hoarsely’ (Vogindroukas et al., 2022: 2370). While these prosodic anomalies do not typically render speech unintelligible, they significantly contribute to the perception of “oddness” in individuals with ASD (Mesibov, 1992; Van Bourgondien, Woods, 1992). This perception can negatively impact the quality of their social interactions (e.g. Grossman et al., 2015) and hinder the development of socio-communicative abilities (Geelhand et al., 2021).

In the following sections, we will provide a detailed discussion of the speech characteristics associated with Autism Spectrum Disorder, with a specific emphasis on prosodic features, which have long been recognized as key clinical markers of autism (Baltaxe, 1984; Fay, Schuler, 1980; Grossman et al., 2010; Olivati et al., 2017; Paul et al., 2005; Shriberg et al., 2001). Furthermore, we will offer an overview on the main findings coming from research using acoustical analysis, a method that allows to objectively assess acoustic correlates of atypical prosodic features in the speech of individuals with ASD.

‘Prosody is a suprasegmental device that can be best described as the “melody” or “rhythm” of speech’ (Grossman et al., 2010), including aspects such as pitch, speech rate, rhythm, stress, and intonation. It plays a crucial role in human social communication by enhancing, emphasising or modifying the meaning of the speech signal (Grice et al., 2023; Shriberg et al., 2001). Speakers use prosody to express multiple functions, such as conveying affect, marking a communicative act, or disambiguating the meaning of a sentence (Bone et al., 2014).

As Grice and colleagues (2023: 2) observed, ‘[s]uccessful communication requires that all participants share the ability to correctly perceive and interpret the prosody of another person’s speech and use prosody appropriately to convey meaning to others’. While typically developing (TD) individuals are able to produce and interpret prosody from the earliest stages of development (Mehler et al., 1988), individuals with ASD often display deficits in both the production and perception of prosody. Moreover, atypical prosodic features tend to persist into adulthood regardless of language levels (Baltaxe, Simmons, 1985; DePape et al., 2012), showing little change over time, even when other aspects of language improve (Paul et al., 2005; Olivati et al., 2017).

However, it is important to avoid overgeneralisations, as difficulties in prosody seem to be limited to certain aspects of communication (Grice et al., 2023). For instance, research suggests that individuals with ASD generally do not experience significant challenges with the formalised expression of grammatical features, such as lexical stress/tone or syntactic structure, because they are determined by rules and, thus, remain stable regardless of context. Conversely, individuals with ASD often struggle with emotional functions of prosody, which are highly variable, as well as pragmatic functions, such as regulating turn-taking or marking information structure. These aspects require greater flexibility in communication and are highly context-dependent. Difficulties with affective and pragmatic functions of prosody may be linked to autistic people’s underlying deficits in social interaction and communication, and to their tendency to follow well-established routines (APA, 2013), rather than navigating the dynamic and unpredictable nature of social contexts.

Many studies on prosody in ASD have predominantly relied on perceptual methods, namely on subjective ratings (e.g. Shriberg et al., 2001; Rapin, Dunn, 2003), often leading to inconsistent findings. For instance, the speech of people with ASD has been variously described as monotone, ‘improperly modulated, dull, and wooden and as having a singsong quality’ (Baltaxe, Simmons, 1985: 104), or as variable, bizarre, or exaggerated (Lord et al., 1994).

In recent years, research has increasingly incorporated acoustic analyses (Dahlgren et al., 2018) to objectively assess speech abnormalities in individuals with ASD. The primary focus has been on acoustic parameters related to pitch, particularly fundamental frequency (F0), including its maximum, minimum, and mean values, as well as pitch range (defined as the difference between max and min F0 values). These studies have both corroborated and challenged previous findings, while also revealing shared tendencies across the spectrum.

A consistent observation is that individuals with ASD often struggle with pitch control, generally resulting in higher mean F0 values (Fusaroli et al., 2017, 2022). Pitch range, however, remains a controversial parameter. Some studies report a significantly wider pitch range in individuals with ASD compared to typically developing population (Bonneh et al., 2011; Filipe et al., 2014; Sharda et al., 2010; Patel et al., 2020), whereas others describe it as narrower (e.g. Biancalani et al., 2023), or irrelevant for distinguishing between ASD and TD groups (Green, Tobin, 2009; Quigley et al., 2016). Interestingly, the finding of an increased F0 range produced by speakers with ASD suggests a more pitch variability, which contrasts with the traditional characterization of their speech as unusually flat or monotone. Additionally, greater inter-individual variability in pitch ranges has been observed within the ASD group compared to the control group (Bonneh et al., 2011; Diehl et al., 2009). These differences seem to persist across the lifespan, from childhood to adulthood (DePape et al., 2012; Diehl et al., 2009; Ochi et al., 2019).

Another common acoustic measure of vocal atypicality in autism is duration, which is generally used to determine speech rate (calculated by the number of syllables pronounced per second), the number and the length of pauses, and the overall duration of speech (Fusaroli et al., 2017). Studies on this parameter offer mixed findings: some report a longer duration of utterances for individuals with ASD compared to those with typical development (e.g. Bonneh et al., 2011; Filipe et al., 2014), while others suggest that duration is shorter (e.g. Oller et al., 2010), or find no significant group differences (e.g. Morett et al., 2016; Quigley et al., 2016). The same goes for speech rate: while some studies indicate no difference between ASD and TD groups (Nadig, Shaw, 2012), others report a slower speech rate in individuals with ASD when compared to TD peers (Bonneh et al., 2011; Patel et al., 2020).

Noteworthy findings also emerge when analysing the length of silent pauses. For example, Imparato et al. (in press) found that Italian autistic school-aged children used longer silent pauses than their typical developing peers, particularly when expected to take the turn, such as after being asked a question. Similar results have been reported by Ochi et al. (2019) in a study involving Japanese autistic adults, suggesting that this characteristic may be consistent across different age groups and cultural contexts. These findings can be interpreted from two different yet interconnected perspectives. On the one hand, they support the hypothesis of a deficit in receptive communication (see Rapin, Dunn, 2003; Vivanti, 2021), as individuals with ASD – especially those who present more severe symptoms – generally struggle to integrate various linguistic and extralinguistic cues in conversation. On the other hand, these findings could indicate a limited ability to overcome communicative challenges using compensatory strategies, such as filled pauses, to maintain the flow of conversation and contribute to the success of the interaction.

Duration is also crucial in investigating rhythm, a suprasegmental feature that significantly contributes to the perception of “odd” speech in autism. Indeed, atypical rhythm is considered a clinical marker of ASD in ADOS (Autism Diagnostic Observation Schedule) (Lord et al.,1999; 2012), a standardized diagnostic instrument. Nevertheless, findings concerning rhythm are highly heterogeneous. For example, Bone and colleagues (2015) found substantial differences in the speech rhythm patterns of adolescents with ASD compared to TD peers, by observing different duration of words, longer pauses and notable variability of syllable duration. In contrast, Patel et al. (2020) did not find any meaningful differences between the rhythmic patterns of autistic and neurotypical speech.

While fundamental frequency and duration have been widely examined in acoustical descriptions of “autistic speech”, especially in research focusing on children, less interest has been given to other acoustical measures, such as jitter, shimmer, and Harmonics-to-noise ratio (HNR).

Jitter and shimmer are closely related: The former is ‘an index of frequency variation of vocal cords from cycle to cycle’, while the latter ‘is an index of the amplitude variation of the speech production from cycle to cycle’ (Maes et al., 2023: 3). Harmonics-to-noise ratio, on the other hand, measures ‘the amount of periodic noise compared to the amount of irregular, aperiodic noise in the voicing signal’ (Styler, 2023: 22). All of these parameters are acoustic correlates of voice quality, which ‘is measured as the difficulty in controlling the vocal fold vibrations, transforming the production into hoarseness, breathiness, and creaky voice’ (Beccaria et al., 2022: 26).

A distinctive voice quality in ASD has been noted since the very early days of the diagnosis. For instance, children with ASD have been described as having hoarse, harsh, and hypernasal voice quality and resonance (e.g. Pronovost et al., 1966).

Research on acoustic correlates of voice quality in ASD has produced varied findings across different age groups. Bone et al. (2014) found that autistic children exhibit more jitter and jitter variability, along with a lower Harmonic-to-noise ratio, while no differences were found in shimmer. They also identified a negative correlation between mean HNR and jitter: as this latter increases, the other decreases. Since aperiodic noise usually detects friction in the vocal tract, a significant decrease of HNR may suggest hoarse or breathy speech, or even laryngeal pathology (Styler, 2023). In contrast, some studies focusing on autistic adults (Kissine, Geelhand, 2019; Kissine et al., 2021) have reported lower jitter and shimmer, and smaller F1-F3 (first, second, third formant) dispersion, suggesting greater stability both in phonation and articulation in comparison to non-autistic adults, indicating that voice quality and control may evolve across lifespan.

Despite significant progress since the earliest reports on autism (Asperger, 1944; Kanner, 1943), challenges persist in understanding prosody in ASD. Several factors contribute to the conflicting descriptions of prosodic features in “autistic speech”, including

• the heterogeneous nature of ASD symptoms and linguistic competence, which makes it challenging to establish definitive speech markers applicable across the spectrum

• the use of diverse methodologies across studies (e.g. voice sampling conditions, type of task), leading to variability in results

• the tendency in many studies to include wide age ranges – often spanning childhood, adolescence, and adulthood – which can affect the reliability of findings.

Given that disordered prosody can be one of the main barriers to social acceptance for individuals with ASD (Shriberg et al., 2001), more comprehensive and systematic research is needed. Future studies should focus on creating more homogeneous sub-groups based on symptom severity, age, and gender to achieve more robust results. Furthermore, the use of more standardized measures across studies is essential to improve the reliability of findings.

A promising field for future research on autism is the implementation of machine-learning algorithms. These training supervised methods can classify the acoustic and prosodic features of speech and determine their significance in the speech production of individuals with ASD, based on the performance of each model (Beccaria et al., 2022). Such approaches could be beneficial to the early detection of autism and to the refining of intervention strategies.

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