ARL-TR-8227 ● DEC 2017

US Army Research Laboratory

Correlational Analysis of Speech 

Intelligibility Tests and Metrics for 

Speech Transmission

by Tomasz R Letowski and Angelique A Scharine

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ARL-TR-8227 ● DEC 2017

US Army Research Laboratory

Correlational Analysis of Speech Intelligibility 

Tests and Metrics for Speech Transmission

by Tomasz R Letowski

Fellow and Researcher Emeritus, ARL

Angelique A Scharine

Human Research and Engineering Directorate, ARL

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Correlational Analysis of Speech Intelligibility Tests and Metrics for Speech 

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14. ABSTRACT

Speech communication can be assessed in many ways. The objective of this review and analysis is to compare the common 

methods of evaluating the quality and intelligibility of speech, and detail the merits and limitations of each. The standard 

speech intelligibility rating scales, perceptual speech intelligibility tests (based on human performance), and technical speech 

intelligibility predictors (based on the input signal transmitted through a communication system or medium) measurement 

methods are described and compared. To establish a basis for comparison between the results of these measures, a common 

intelligibility scale is described. Its use in the comparison of scores obtained for different measures of speech intelligibility is 

discussed, as well as its use to determine which test is optimal for a given environment. This analysis is intended to serve as a 

resource for users of standard speech intelligibility measurement methods.

15. SUBJECT TERMS

speech communication, speech intelligibility, speech quality, perceptual measures, objective measures, common intelligibility

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Contents

List of Figures iv

List of Tables iv

1. Introduction 1

2. Characteristics of the Speech Signal 1

3. The Concept of Speech Intelligibility 2

4. Speech Intelligibility and Speech Quality 6

5. Speech Intelligibility Assessment 8

5.1 Speech Intelligibility Rating 9

5.2 Perceptual Tests of Speech Intelligibility 13

5.2.1 Phonetically Balanced Word Test (PBWT) 15

5.2.2 Diagnostic Rhyme Test (DRT) 16

5.2.3 Modified Rhyme Test (MRT) 17

5.3 Technical (Predictive) Tests of Speech Intelligibility 18

5.3.1 Articulation Index (AI) 19

5.3.2 Speech Transmission Index (STI) 20

5.3.3 Speech Intelligibility Index (SII) 22

6. Common Intelligibility Scale 24

7. Selecting a Speech Intelligibility Test and Interpreting Its Score 27

8. Discussion and Conclusions 31

9. References 35

List of Symbols, Abbreviations, and Acronyms 48

Distribution List 50
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List of Figures

Fig. 1 Relative distributions of speech power and speech intelligibility across 

frequency scale (male voice; normal voice effort) ............................... 4

Fig. 2 Diagram of a speech communication system (Letowski)..................... 4

Fig. 3 The effect of telephone bandwidth on transmission of vowel /o/ by 

male talker with voice fundamental frequency Fo = 100 Hz (Rodman 

2003) ..................................................................................................... 5

Fig. 4 Relationships between various speech intelligibility scales expressed 

on the common intelligibility scale (Knight 1994)............................. 25

Fig. 5 CIS and STI values defining STI categories proposed by Houtgast and 

Steeneken (1984; 1985) ...................................................................... 28

Fig. 6 CIS and STI potential relation with category rating (CCR) scale used 

as the MOS scale in telephone networks quality evaluation............... 28

Fig. 7 Performance intensity functions obtained for the callsign acquisition 

test (CAT) and the MRT in white noise (Blue-Terry and Letowski 

2011) ................................................................................................... 33

Fig. 8 Theoretical shapes of MRT and CAT performance intensity functions 

derived for data from the CAT and MRT speech intelligibility 

measures (Blue-Terry et al. 2012) ...................................................... 33

List of Tables

Table 1 Speech levels (dB A-weighted) of male and female talkers at a 1-meter 

distance. Levels listed by Berger et al. (2003) and ISO-9921 (ISO 

2003) standard (numbers in parentheses) ............................................. 2

Table 2 Mean opinion score (MOS) and degradation category rating (DCR) 

scales................................................................................................... 10

Table 3 Comparison category rating (CCR) scale used in telephone network 

quality evaluations.............................................................................. 11

Table 4 Ranges of speech intelligibility and corresponding values on the STI 

and CIS scales..................................................................................... 27

Table 5 Required speech intelligibility scores as per ISO 2470:2007 (ISO 

2007b) ................................................................................................. 29

Table 6 Intelligibility criteria for voice communication systems.................... 30

Table 7 Intelligibility score requirements as a function of communication 

criticality ............................................................................................. 30
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1. Introduction

Speech communication can be assessed in a variety of ways. The objective of this 

review is to summarize the most common measures of speech intelligibility, stress 

the strengths and limitations of each of these measures, and establish a basis for 

comparison between the results of these measures. How speech is measured 

depends in part on the objective of the measurement—whether it is to establish 

good speech quality or speech intelligibility—and in part on the restraints and costs 

associated with the measurement. This review summarizes the different types of 

measurements of speech quality and speech intelligibility and discusses their 

relationship. The standard speech intelligibility rating scales, perceptual speech 

intelligibility tests (based on human performance), and technical speech 

intelligibility predictors (based on the input signal transmitted through a 

communication system or medium) are described and compared. The merits and 

limitations associated with each of these measures are discussed with an aim to 

outline the criteria on which test selection should be founded. The optimal measure 

will depend on the absolute need for speech intelligibility, the acoustic environment 

in which communication will occur, as well as the cost of measurement and 

regulatory requirements associated with the evaluation. Consequently, a common 

speech intelligibility scale is described that allows data scores from different 

measures of speech quality and speech intelligibility to be compared, and allows 

the user to predict performance on one measure based on the results of another. 

This analysis is intended to serve as a resource for users of standard speech 

intelligibility measurement methods and as a guide in selecting speech 

intelligibility tests and performance criterion for specific applications.

2. Characteristics of the Speech Signal

Speech is a form of language (communication code) that uses vocally produced 

sounds to convey thoughts, meanings, and feelings. To communicate by speech, 

speech sounds must be both produced and perceived. Speech production refers to 

the process by which predetermined vocalized sounds are produced by the talker 

and organized in sequences forming communication signals. Speech perception is 

the process by which the listener is able to hear and interpret (understand) the 

message encoded in the speech signals.

The effective design and use of audio communication systems requires some 

knowledge of the physical properties of speech and the rules that govern the human 

perception of speech. The 2 main physical descriptors of speech signal are its sound 

intensity and spectral content. The long-term average sound intensity levels of 
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phonated speech produced with various levels of vocal effort are listed in 

Table 1. However, individual phonemic components of speech vary greatly in their 

intensity with vowels carrying much greater energy than consonants. The strongest 

vowel, /aw/, as in word all, is about 28 dB more intense than the weakest consonant, 

/th/, as in word thin (Staab 1988). Whispered (unphonated) speech levels are in the 

order of 40 dB(A) (Traunmüller and Eriksson 2000) but this kind of speech is not 

used in formal communication.

Table 1 Speech levels (dB A-weighted) of male and female talkers at a 1-meter distance. 

Levels listed by Berger et al. (2003) and ISO-9921 (ISO 2003) standard (numbers in 

parentheses)

Vocal Effort Male Talker Female Talker

Low (relaxed) 52 (54) 50

Normal 58 (60) 55

Raised 65 (66) 62

Loud 76 (72) 71

Shout 89 (78) 82

A person’s vocal level effort depends on the visual and auditory clues stemming 

from the distance (real or perceived) to the listener and the emotional state of the 

talker. In noisy environments vocal effort is naturally higher (raised, loud, or 

shouted) than in quiet (normal) environments, because talkers involuntarily raise 

their voices to the level needed for them to hear themselves (Lombard effect*; 

Fairbanks 1954; Summers et al. 1988). Conversely, talkers wearing hearing 

protectors reduce their vocal efforts by about 3 dB, compared to when unprotected, 

if the background noise level exceeds 75 dB A (ISO 2003).

The speech levels listed in Table 1 are the levels measured in front of the talker’s 

mouth. However, the vocal source is quite directional and the levels at the talker’s 

back may measure up to 5–7 dB lower. This difference is relatively small at lowand mid-frequencies but sharply increases for spectral content at higher frequencies 

(consonants). 

3. The Concept of Speech Intelligibility

The main criterion for a speech system’s effectiveness is its speech intelligibility, 

that is, the degree to which speech can be understood by a listener. According to 

Côté (2011, p. 11), speech intelligibility is a measure of how much of a message 

has been extracted from the recognized phonemes (the smallest units of speech). In 

other words, it indicates the extent to which words and sentences can be understood 

 * The Lombard effect, or Lombard reflex, is the involuntary tendency of speakers to increase their 

vocal effort when speaking in loud noise to enhance the audibility of their voice.
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(Viswanathan and Viswanathan 2005). Speech intelligibility is usually expressed 

as the fraction (percentage) of speech units received correctly. It primarily depends 

on speech energy in the 1000–6000 Hz band and is poorly correlated with the 

overall frequency distribution of speech power that extends from about 80 to 

8000 Hz (MIL-HDBK-1908B 1999) with the main concentration of average speech 

spectrum energy in the range of 200–600 Hz. The relationship between the speech 

power spectrum and the contributions of various spectral regions to speech 

intelligibility is shown in Fig. 1. Speech power above 1000 Hz is equal to only 

about 5% of the total speech power but it contributes about 60% to speech 

intelligibility (Fletcher 1953). Speech energy below 250 Hz contributes only about 

2% to overall speech intelligibility (Gerber 1974, p. 244). The spectral centroid 

(center of gravity) of the speech spectrum below and above of which speech power 

equally contributes to speech intelligibility in the English language is close to 

1500 Hz (Fletcher 1953) [1700 Hz (French and Steinberg 1947)] and varies 

depending on language. 

Similarly to overall speech power, vowels and consonants also differ widely in

spectral content. Consonants carry most of the information contained in English 

speech as well as in other languages. Consonants contain mostly high frequency 

(above 1500 Hz) speech energy, but this energy is relatively small in comparison 

to that of the whole speech spectrum (Fig. 1) and thus, consonants are easy to 

misjudge. Vowels, having much greater power and mostly low- and mid-frequency 

spectral energy, are usually easily heard. Note that changes in voice effort level not 

only affect the resulting overall speech level but also speech spectrum. Greater 

vocal effort results in elevated mid-frequency spectral regions in female and male 

voices and a higher overall fundamental frequency in male voices (Letowski et al. 

1993). Since the mid-frequency spectral region contains mostly vowel energy while 

consonants are high frequency sounds, an increased vocal effort typically reduces 

the intelligibility of speech. Note also that in whispered speech the overall sound 

power is low and the speech spectrum is almost flat, resulting in more frequent 

vowel confusion than occurs for normal speech.
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Fig. 1 Relative distributions of speech power and speech intelligibility across frequency 

scale (male voice; normal voice effort)

In general, the intelligibility of speech signals received by a listener depends on 

speech articulation by the talker, properties of the surrounding environment or 

communication system, and the listener’s ability to understand incoming speech 

signals. Figure 2 shows a basic diagram of the relationship between terms related 

to the intelligibility of speech transmitted by a communication system 

(communication channel).

Fig. 2 Diagram of a speech communication system (Letowski)
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The communication system shown in Fig. 2 can be any physical environment (air, 

electroacoustic, optoelectronic, etc.) connecting the talker and the listener whether 

they are people or machines. When the concept of speech intelligibility is applied 

to speech degradation caused by a communication system, it should be assumed 

that both the talker and listener are the ideal sender and receiver, respectively. If 

this sender and receiver were connected by the ideal speech communication system, 

all of the speech signals voiced by the talker would be correctly reported by the 

listener. These speech signals can be said to have 100% speech intelligibility. 

Therefore, any departure from 100% speech intelligibility in speech transmission 

through a communication system connecting ideal source (talker) and receiver 

(listener) is assumed to be due to the limitations of this system. In the case of real 

talker and real listener, they both contribute to the degradation of speech in the 

communication channel, and both the limited talker’s speech intelligibility and the 

limited listener’s speech recognition ability need to be considered and measured. 

A long list of the technical factors that affect speech intelligibility during speech 

transmission between ideal source and receiver can be found in the ANSI/ASA S3.2 

standard (ANSI/ASA 2014). The most dominant of them are limited transmission 

bandwidth, type and level of background noise (including crosstalk), and 

oscillations and echoes. The effect of telephone bandwidth (300–3400 Hz) (ITU 

2003; TIA 2006) on speech spectrum transmission is shown in Fig. 3.

Fig. 3 The effect of telephone bandwidth on transmission of vowel /o/ by male talker with 

voice fundamental frequency Fo = 100 Hz (Rodman 2003)

The upper limit of frequency bandwidth is considered the most critical technical 

parameter of a transmission channel with respect to speech intelligibility. Vowels, 

which are strong wideband sounds, do not contribute much to speech intelligibility 

at the frequency range below 200–300 Hz. However, excluding the frequency range 
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below 300 Hz affects the quality and realism of transmitted speech. Consonants, 

which are low energy speech sounds, are predominantly made up of high frequency 

spectral content. Therefore, good speech intelligibility requires transmission 

bandwidth extending up to 7000 Hz. For single syllable words, limiting the upper 

range of the bandwidth to 3400 Hz results in speech intelligibility as low as 75%. 

A bandwidth of 7000 Hz is needed to achieve speech intelligibility better than 95% 

(French and Steinberg 1947). Video conferencing audio connections commonly 

transmit frequencies up to 6800 Hz. Voice over internet protocol (VoIP) telephony,

as well as cellular telephone networks, are also moving to a wider (150–6800 Hz)

bandwidth using compressed and uncompressed codec techniques (TIA 2015ab; 

TIA 2011).

The presence of noise makes the perception of speech difficult due to the strong 

masking of the high-frequency components of speech. As a result, the consonants, 

which are low-energy, high-frequency sounds, are more prone to masking than the 

high-energy, wide-spectrum vowels. Such contaminated speech is still audible but 

not clear. Thus, speech may be detected but not recognized at low speech-to-noise 

ratios (SNRs). In general, an SNR of at least 6–10 dB is needed across the 

frequency range of speech (200–7000 Hz) to make speech sufficiently intelligible 

for communication (~90% word recognition score) (Moore 1977; NASA/SP-2010-

3407 2010). 

Similarly, the presence of excessive reverberation (reflected sounds from the 

boundaries of a space), as well as echoes in the communication system, extend the 

effective duration of transmitted sounds and cause both concurrent and temporal 

masking of subsequent sounds. Conversely, nonlinear distortions, such as 

compression and level limiting, have relatively small effect on speech intelligibility 

and in some cases may improve speech intelligibility (Moore 1977). 

4. Speech Intelligibility and Speech Quality

In making assessments of speech communication systems, speech intelligibility 

needs to be differentiated from speech quality, which is another perceived 

characteristic of speech. The judgment of speech quality is an expression of general 

satisfaction. It expresses the listener’s preference for, or discomfort with, the 

perceived speech. According to Jekosch (2005), the evaluation of speech quality is 

a comparison of the received speech to the desired speech. Unfortunately, the 

literature disagrees about the factors that form the main perceptual dimensions of 

speech quality and their relationship to speech intelligibility. Côté (2011) has 

analyzed several publications related to this topic and concluded that speech quality 

has 4 main components: loudness, speech continuity, noisiness, and coloration. 
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Speech intelligibility was included as another minor component. However, the most 

prevailing view is that speech quality has 2 main components: naturalness (voice 

quality) and speech intelligibility (Kraft and Portele 1995; Lewis 2001). 

Naturalness is the degree to which the speech signal resembles normal clear human 

speech (Viswanathan and Viswanathan 2005). Voice quality has been defined as 

the characteristic color of speech (Keller 2005) and can be equated with voice

pleasantness or voice naturalness but has to be differentiated from voice timbre, 

which specifies voice character without qualifying it. Both quality and timbre have 

multidimensional character and can be evaluated along several perceptual 

dimensions such as brightness, loudness, nearness, and so on. Most of them are the 

same in both domains but expressed on qualitative better-worse (quality) and 

quantitative more-less (timbre) scales (Letowski 1992).

Since speech intelligibility is frequently considered to be a component of speech 

quality, some methodologies that are used for assessing speech quality can also be 

used for assessing speech intelligibility. However, speech quality judgments should 

not be used in lieu of speech intelligibility judgments or vice versa because such 

judgments may be misleading. Although in some cases speech quality may be 

highly correlated with speech intelligibility (e.g., Leijon et al. 1991; Sullivan et al. 

1988), in other cases it may not be (e.g., Goodman et al. 1978; Punch and Parker 

1981; Byrne and Cotton 1988). Payton and Braida (2005) studied the effects of 

slow-acting wide dynamic range compression (WDRC) on speech intelligibility 

and speech quality judgments and observed that while WDRC can improve speech 

intelligibility, it decreases speech quality ratings as the compression ratio increases. 

The authors concluded that judgments of speech quality should accompany 

assessments of speech intelligibility to assure that speech transmission optimizes 

both comfort and intelligibility. 

Preminger and Van Tasell (1995) observed that when large differences in speech 

intelligibility exist they dominate all other perceptual judgments and in such cases 

speech quality and speech intelligibility are highly correlated. However, this may 

not be the case when differences in speech intelligibility are relatively small. For 

example, Kondo (2011) compared results of a speech intelligibility test (DRT) and 

mean opinion score (MOS) judgments of speech quality and reported the Pearson 

correlation coefficients of 0.36–0.47 for female and male speech mixed with white 

and babble noise. In addition, the perceived contributions of voice quality and 

speech intelligibility to overall speech quality may differ greatly among judges, 

leading to large variability in the observed data. Unfortunately, in many 

publications and test reports, the term speech quality is used sui generis when the 

authors mean speech intelligibility. The comment above applies not only to speech 

quality but also to other similar criteria, such as clarity and comprehensibility, 
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sometimes used in lieu of speech intelligibility as synonyms. Therefore, in judging 

speech intelligibility, only the speech intelligibility term should be used as an aim 

of the rating and other similar terms should be avoided. Eisenberg et al. (1998) 

compared perceptual judgments of speech clarity and speech intelligibility of the 

same speech material and observed that the ratings were highly related but differed 

substantially in magnitude with intelligibility ratings consistently exceeding clarity 

ratings. Recently, Reinhart and Souza (2016) found a high correlation between 

perceptual judgments of speech clarity and speech recall scores (termed speech 

intelligibility) but noticed that the recall scores were dependent on the capacity of 

the listener’s working memory while the rating scores were not. In cases like this it 

is impossible to determine whether the judges equated clarity with perceived 

intelligibility. Some authors also believe that speech intelligibility differs from 

speech comprehensibility, which includes contextual information (Yorkston et al.

1996; Hustad 2008; Fontan et al. 2015). 

In considering terminological issues, the reader needs to be aware that some authors 

regard speech intelligibility as having 2 components—audibility and clarity—that

contribute to overall intelligibility while other authors consider speech 

intelligibility as having an even larger number of dimensions. For example, De 

Bodt et al. (2002) argued for 4 main dimensions: voice quality, articulation, 

nasality, and prosody. All of these factors indicate that rating studies need to be 

done very carefully with very clear instructions and with sufficiently trained judges. 

5. Speech Intelligibility Assessment

In reference to Fig. 2,speech intelligibility tests may be used for 3 distinct purposes:

1. To assess the talker’s ability to articulate speech and to diagnose potential 

voice/speech disorders;

2. To assess the listener’s ability to recognize speech sounds and to assess the 

listener’s hearing deficiencies affecting speech communication (speech 

audiometry); and

3. To assess the capabilities of a specific transmission channel (e.g., 

electroacoustic system, reverberant room, etc.) for speech communication 

and the degree of potential speech degradation.

The focus of this review and analysis is on the third purpose—assessment of 

transmission channels; however, some limited inferences regarding 2 other 

purposes—speech articulation and speech recognition—will also be made. 
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In general, speech intelligibility evaluation methods can be divided into perceptual 

methods, which require natural or synthetic speech and human listeners, and 

technical (objective) methods, which use artificial signals and the technical 

parameters of the communication system to predict speech intelligibility according 

to some internal criteria. Perceptual methods can be further divided into rating 

(ordinal scale) and speech recognition measurement (ratio scale) methods based on 

an estimated or calculated percent correct of listener’s responses. 

In the case of perceptual tests, speech material should be phonetically (or 

functionally) balanced to allow equal stress on all elements of transmitted speech. 

While phonetic balance is especially important for talker and listener assessments, 

functional balance is important for testing transmission channels used for specific 

applications. 

Transmission channels should be tested under normal operational conditions. For 

comparison, in speech audiometry, the talker’s and listener’s ability to produce and 

receive speech, respectively, should be evaluated under optimal conditions where 

the SNR ratio is at least 40 dB.

5.1 Speech Intelligibility Rating

Rating is a perceptual categorization procedure by which an object or various 

objects are assessed according to their perceived grade or rank using a verbal or 

numeric scale of successive categories. The number of successive categories can 

be freely specified but it should be small enough to be manageable and large enough 

to allow meaningful differentiation of the objects. The speech material can be 

operational phrases, sentences or connected speech that are representative of the 

specific application. They may be prerecorded, synthetic, or delivered live but must 

be presented in a way consistent with the purpose of the assessment.

The most widely used statistical rating system for speech intelligibility is the MOS.

In a MOS test, a number of people rate their perceptions on a 5-step scale from 1

(bad) to 5 (excellent) and their ratings are statistically averaged. The MOS is an 

absolute category rating system in which a listener makes a rating after listening to 

a single item and no comparisons are made. Only integer numbers should be used 

for ratings (ITU-T 1996). Other rating scales exist that use 7-, 9-, and 11-steps, but 

these are only recommended for highly trained judges (Miller 1956; Osgood et al. 

1957; Eisler 1966; Warr and Knapper 1968).
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The MOS was originally developed for the assessment of speech quality in 

telephone networks. Since then its use has been extended to various audio and video 

signals and systems where a degree of departure from an ideal reference signal 

needs to be known. When the MOS is used to assess the quality of transmission 

systems, it is sometimes referred to as the “transmission quality” or “quality of 

service (QoS) rating system. Obviously, the same rating system may be used for 

assessment of other signal or system properties, such as speech intelligibility, if 

only the assessment criterion is clearly stated.

Since the MOS can be used for an assessment of various systems and signals, it has 

a number of qualifiers appended to its name to indicate its specific application. 

When the MOS is used to assess listening quality of a signal it is referred to as the 

MOS-LQS (or MOS-LQ), that is, the MOS-Listening-Quality Scale (ITU-T 2003).

The MOS-LQS scale is commonly used in the telephone industry to rate 

performance of telephone network connections and is described in ITU-T 

Recommendation P.800 (ITU 1996). It can be used alone or together with 2 

supporting scales: the listening-effort scale (MOS-LES) and the loudnesspreference scale (MOS-LPS)—both being similar 5-step scales—to assess various 

aspects of the telephone transmission. Other versions of the MOS include the MOSCQS (MOS conversational-quality scale) or the MOS-PQS (MOS picture-quality 

scale). In case of speech intelligibility ratings, the MOS scale may be qualified as 

the MOS-SIS (MOS speech intelligibility scale).

The MOS scale in its original form, and in the paired comparison form known as 

the degradation category rating (DCR) scale, are both shown in Table 2.

Table 2 Mean opinion score (MOS) and degradation category rating (DCR) scales

Rating MOS DCR

5 Excellent Inaudible

4 Good Audible but not annoying

3 Fair Slightly annoying

2 Poor Annoying

1 Bad Very annoying

In the paired comparison judgments (DCR scale), the reference condition is always 

presented first and the reference condition must always be better that the tested 

condition. This limits the use of the DCR.

Goldberg and Riek (2000, p. 118) observed that most speech coding systems score 

between 3 and 4 on the MOS scale and that a coder scoring above 4 provides very 

good speech quality. More recently, Waidyanatha et al. (2012) suggested that MOS 

values equal to or greater than 4 should be the desired speech quality score for 
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telephone network connection and for emergency communication systems. Typical 

modern high quality codec systems such as the G.711 score about 4.4 on the MOS 

scale while the G.729, which involves greater signal compression, scores 4.1

It has to be stressed that despite its name, the MOS is a rating (assessment) system 

and not sensu stricto a scoring (measurement) system. The MOS scale, however, 

meets all of the criteria for a well-designed rating scale, having an odd number of 

categories and clearly defined levels. The 5-item category range is large enough to 

be sensitive to differences and small enough to be easily memorized and result in 

fairly repeatable judgments. 

Absolute category ratings used in MOS testing are generally less sensitive than 

estimates obtained by a paired comparison technique (e.g., Munson and Karlin 

1962; Eisenberg et al. 1997). When appropriate, the International 

Telecommunication Union (ITU) recommends that MOS ratings be combined with 

paired comparison techniques to create an extended 2-directional version of MOS 

scaling. The resulting scale is a 7-step extended MOS scale called the Comparison 

Category Rating (CCR) scale and is shown in Table 3. Such 2-directional 

comparative scales are considered less taxing for the human information processing 

system than single-directional absolute rating scales (e.g., Osgood et al. 1957). The 

CCR scale is used in telephone network quality evaluations. The listener compares 

a test sample to a standard reference sample that can be of a higher or lower quality 

than the test sample (e.g., Rothauser and Urbanek 1965). The resulting speech 

quality value is referred to as the Comparison Mean Opinion Score.

Table 3 Comparison category rating (CCR) scale used in telephone network quality 

evaluations

CCR Rating Quality of the second stimulus compared to the first one

3 Much better

2 Better

1 Slightly better

0 About the same

–1 Slightly worse

–2 Worse

–3 Much worse

The main advantages of the MOS rating system for assessment of speech 

communication systems are: 1) comprehensive assessment of the system, 2) face 

validity of the data, and 3) simplicity of testing. However, MOS tests require trained 

listeners to provide meaningful scores and to avoid large data variability. Further, 

MOS ratings do not indicate what types of problems were encountered by 
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the listeners unless additional attributes are also rated. Thus, the test results are open 

to interpretation by equipment manufacturers and installers since they provide no 

contextual information about the causes of poor speech quality.

When used for speech intelligibility assessment, the MOS scale, as well as all other 

rating scales, is not sufficiently precise for some applications. That is, the degree to 

which it can predict speech intelligibility scores is quite limited in comparison to 

other scoring methods. Further, it is a low-level, ordinal scale that limits the types 

of statistical analyses that can be performed on the data. Conversion of ordinalscale numbers into ratio-scale numbers is possible, but requires a large sample size 

to assure small confidence intervals, making data collection both costly and timeconsuming. 

Despite the limitations described above, categorical ratings of speech intelligibility, 

such as those made with the MOS scale, have been reported to be “valid measures 

of speech recognition” (Purdy and Pavlovic 1992, p. 254). Several authors have 

reported good agreement between speech intelligibility ratings and intelligibility 

measured as percent correct speech recognition (e.g., Payton and Braida 2005). For 

example, in studies conducted at the Harvard Psycho-Acoustic Laboratory during 

World War II, intelligibility ratings obtained from a small number of trained 

listeners correlated well with speech recognition scores obtained with the same 

listeners (Stevens et al. 1944). Therefore, numerical scores produced by the various 

speech intelligibility tests are frequently equated to MOS categories when 

evaluating their results. 

Using a percentage rating scale (0–100%) with assumed ratio scale properties 

allows one to avoid the limitations of ordinal scale. A percentage rating scale 

requires that the judged range has 2 physical ends and the judged quantity can be 

expressed as a fractional number. In the case of speech intelligibility, this scale is 

referred to as the Speech Intelligibility Rating (SIR) scale. Judgments made on this 

scale express the listener’s belief that a certain percentage of the message was 

properly heard. Percentage scale scores for both syllables (Rankovic and Levy 

1997) and sentences (Speaks et al. 1972; Cienkowski and Speaks 2000) showed 

very good agreement with speech recognition scores even when using untrained 

listeners. Because SIR tests are much less time consuming than speech recognition 

tests (described later), they can be reliably applied in many situations where data 

collection needs to be completed in a short time and with ad-hoc listeners. 

A well-documented, freely available, sentence intelligibility test involving SIR 

scale has been published by Cox and McDaniel (1989). The test consists of several, 

specially-written connected discourse passages that are read (presented) to a 

listener who subsequently judges, on a scale from 0 to 100%, how intelligible the 
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13

passage was (see also McDaniel and Cox 1992). The test was initially developed 

for hearing aid selection but has since been used for judging distortions in speech 

transmission systems and the speech production and perception of cochlear implant 

users (e.g., Allen et al. 2001; Zhou et al. 2013). 

There are relatively few studies comparing speech intelligibility rating data with 

intelligibility test scores but all of them indicate that intelligibility ratings closely 

approximate objective scores on a sentence transcription task when performed by 

normally hearing listeners (e.g., Yorkston and Beukelman 1978; Cox et al. 1991). 

However, Cox et al. (1991) reported that hearing impaired persons have a tendency 

to rate speech intelligibility as lower than indicated by their objective scores.

5.2 Perceptual Tests of Speech Intelligibility

Perceptual tests of speech intelligibility directly measure the percentage of correctly 

understood speech items and several such test methods are included in international 

and national standards. In these tests speech recognition responses are collected 

from human listeners presented with some forms of human speech: phonemes, 

syllables, words, phrases, sentences, or paragraphs. Speech material can be live, 

recorded, or synthetic. In addition to assessment of overall speech intelligibility, 

collected data can be analyzed to provide information about type of errors and about 

specific deficiencies that were encountered. However, their validity is limited to the 

speech material and speech complexity level (speech unit).

The speech unit most commonly used to assess speech intelligibility of the 

communication systems is the word, although phonemes, syllables, and sentences 

have also been used. For example, IEEE Recommended Practice for Speech Quality 

Measurements (IEEE 1969; Rothauser et al. 1969), lists 72 lists of 10 phonetically 

balanced sentences (Harvard Sentences) for communication systems testing. The 

internet Open Speech Repository makes available standardized and repeatable 

recordings of female and male voices reading the IEEE sentences 

[http://www.voiptroubleshooter.com/open_speech/american.html]. However, as 

the most common speech intelligibility test used to measure speech transmission 

over communication systems is some form of word test, this will be the focus of 

this section.

The preferred status of word tests in evaluation of speech communication systems 

may be explained by historically easy access to word level tests, attractiveness of 

the meaningful short test items to the listening panel, and easy mathematical 

treatment (e.g., by binomial model) of the data that are scored as either correct or 

incorrect and not by parts; for example, sentences scored as percentages of words 

correctly repeated (Thornton and Raffin 1978; Raffin and Schaffer 1980; 
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14

Boothroyd and Nittrouer 1988). It is also important to mention in this context the 

assumption that speech recognition (speech intelligibility) is a single construct 

(Bilger 1984); “Because all speech-recognition tests evaluate the same construct, 

scores on all tests must be related and, therefore, scores on one speech-recognition 

test should be predictive of scores on other tests,” (Olsen et al. 1997, p. 183). This 

assumption has been validated in some studies within 6–12% error for phoneme, 

word, and sentence tests using the same basic speech material presented both at low 

and high SNRs and regardless of the hearing status of the listener (Boothroyd and

Nittrouer 1988; Olsen et al. 1997). However, it has to be remembered that the 

differences in speech material and in number of test items used in various speech 

tests call for caution in making any generalization regarding test equivalency. 

The predictability and reliability of word-level tests depend on the test vocabulary 

size and the number of response alternatives offered to the listener. Larger 

vocabulary sizes make the test more predictive and reliable. In terms of the 

listener’s response alternatives, all tests can be categorized as either closed-set or 

open-set response alternatives. An open-set test assumes an unlimited set of 

available alternatives and the listener must make an unconstrained guess of the 

presented word. The intelligibility (INT) score is calculated as

IIIIII(%) = 100

TT (RR), (1)

where T is number of items in the test and R is number of correct responses. In a 

closed-set test, the listener is given a set of response alternatives (words) and must 

select one of them in response to the presented item. The INT for a closed-set test 

is usually calculated as

IIIIII(%) = 100

TT �RR − WWNN−1

�, (2)

where W is the number of wrong responses and N is the number of listener response

alternatives. The second part of Eq. 2 is a correction for guessing. 

Assuming a binomial distribution of the test scores (Thornton and Raffin 1978), its 

standard deviation (SD) can be calculated as

SSSS = 100√[

ee(1−ee)

nn ], (3)

where n is the number of words and e is the word error rate (percent error).

Three word-level speech intelligibility tests are included in the American National 

Standards Institute (ANSI/ASA 2014) standard for use in the evaluation of speech 

transmission systems:
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1) Phonetically Balanced Word Test (PBWT)

2) Diagnostic Rhyme Test (DRT)

3) Modified Rhyme Test (MRT)

Other similar tests include the Central Institute for the Deaf (CID) W-22 Auditory 

Test (Hirsh et al. 1952), the Northwestern University Auditory Test No. 6 (Tillman 

and Carhart 1966), the California Consonant Test (Owens and Schubert 1977), and 

the US Army Research Laboratory (ARL) Callsign Acquisition Test (CAT) (Rao 

and Letowski 2006). 

5.2.1 Phonetically Balanced Word Test (PBWT)

The PBWT is a semi-open word test consisting of 1000 monosyllabic words 

divided into 20 phonetically balanced lists of 50 words. Each word is embedded in 

the same carrier phrase such as “Would you write ___ now.” The words of each list 

are presented in a different random order each time the list is used. In a single test 

trial, a word from the list is presented to the listeners who write down what they 

think the word was. The intelligibility score (PB) is calculated as it would be in the 

case of an open-set test. The test, originally developed by Egan (1948) at the 

Harvard Psycho-Acoustic Laboratory, was originally known as the Harvard PB-50 

test. The minimum test length is a one-word list but using 2–3 lists is recommended 

since this results in more reliable data.

The phonetic balance of speech sounds is very important for all speech tests since 

increasing the frequency of a sound's occurrence increases the probability of its 

recognition and selection of a related word. Similarly, words that appear more 

frequently than others in the test are easier to recognize. Rubenstein and Pollack 

(1963) refer to it as the “word predictability” and stated that within certain limits,

word intelligibility “is a simple power function of [word] probability,” (p.157). 

They also noted that successive repetition of the same word improves its 

intelligibility. Miller et al. (1951) observed that repeating the word 3 times had the 

same effect as improving SNR by 2 dB. Hilkhuysen et al. (2012) presented short 

sentences up to 8 times and observed an improvement in recognition during the first 

5 presentations (from 36 to 51%). 

The PBWT is difficult to accurately administer and requires a long training time 

(typically 10–20 h) before listener responses stabilize at their highest level. The 

more distortions within a communication channel, the greater their effects on the 

recognition of unfamiliar words relative to familiar words (Epstein et al. 1968). 

Therefore, a long training time is required to familiarize listeners well with all the 

words (1000) and their pronunciations. However, the PBWT is the most accurate 
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of all the standardized tests and is recommended for use when high data accuracy 

and sensitivity are required. It is particularly sensitive to the SNR; a relatively small 

change in SNR causes a large change in the intelligibility score. In ANSI/ASA S3.2

the PBWT uses 1000 words but there are also shorter versions, including one with 

256 words for testing less critical systems. 

5.2.2 Diagnostic Rhyme Test (DRT)

The DRT uses monosyllabic English words and consists of 96 rhyming word pairs 

(Voiers 1977, 1983). Almost all of the words have the consonant-vowel-consonant 

(CVC) structure. The words in a pair differ only in their initial consonant sounds 

(i.e., veal-feel) following the concept of the rhyme test developed by Fairbanks 

(1958). In a single test trial, the listener is visually shown a pair of words and asked 

to identify which one of these 2 words is subsequently spoken by the talker. The 

correct word is always one of the 2 words presented visually. Since it is a closedset test, the closed-set correction for guessing given in Eq. 2 is used. A carrier 

sentence is not used.

The main strengths of the DRT are its simple administration, short training time 

(1–2 h), and task simplicity. Because the words are presented without a carrier 

phrase, the test administration is very time efficient. It is also a phonetically 

balanced test that takes into account both word similarity and phonetic frequency. 

In addition, the test results can be reviewed in terms of distinctive features of speech

derived from the Jacobson et al. (1952) distinctive feature system. To calculate the 

speech intelligibility score, the listener’s responses are categorized according to 6

phonemic features: voicing, nasality, sustention, sibilation, graveness, and 

compactness. These category scores provide diagnostic information about 

transmission system deficiencies. At the final stage of score calculation, the 6

scores are averaged together to provide an overall measure of system intelligibility. 

Unfortunately, the DRT measures speech intelligibility for only initial consonants. 

It has been established that initial consonants of normal speech are more easily 

recognizable than final consonants (Nye and Gaitenby 1973). Further, the DRT has 

been criticized as having relatively poor sensitivity: a 2-alternative closed-set test 

being “too easy to produce meaningful results,” (Webster and Allen 1972, p. 36).

However, due to its providing information about the distribution of distinctive 

feature errors, the DRT has been extensively used in testing speech coders for both 

military (e.g., by Department of Defense Digital Voice Processor Consortium) and 

civilian applications (e.g., Schmidt-Nielsen 1992).
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5.2.3 Modified Rhyme Test (MRT)

The MRT is a monosyllabic word test developed by House et al. (1965) that uses

300 monosyllabic English words divided into 50 6-word lists of rhyming or similarsounding words (i.e., pin, sin, tin, fin, din, win). Almost all words of the test have 

a CVC structure. The words in each group differ in either the sound of the initial 

(25 groups), or final (25 groups), consonant. The spoken words are typically 

presented in a carrier phrase “You will mark ___ now.” A listener is shown 6

rhyming response alternatives and asked to identify the spoken word. The MRT has 

structural similarity to the rhyme test developed by Fairbanks (1958) who used 5-

word groups of rhyming words; however, words in the rhyme test and the DRT 

only differed in their initial consonant sound. 

The MRT has gained wide popularity and is typically used to measure the 

communication performance of military and aviation communication systems. As 

a closed-set (N=6) test, the MRT is easy to administer and it does not require as 

much training as for the PBWT. Further, listeners show very little evidence of 

learning during repeated tests (House et al. 1965; Williams and Hecker. 1967; Nye 

and Gaitenby 1973). MRT scores tend to be lower for final than for initial 

consonants, which is the reason why MRT is frequently preferred over the DRT for 

speech communication system assessment. Logan et al. (1989, p.579) used the 

MRT to compare speech intelligibility of several speech synthesizers and reported 

that the “MRT can be used as reliable measure of the segmental intelligibility of 

synthetic speech produced by a variety of text-to-speech systems.” Normally, the 

MRT requires only a short training time of 1–2 h but in the case of synthetic speech,

the learning effect during tests is easily noticeable (Nye and Gaitenby 1973). 

Limitations of the MRT include “an imbalance in the number of times various 

consonants are presented, an uneven representation of initial and final consonants 

(with 2 consonantal phonemes omitted entirely), and an incomplete use of vowel 

environments” (Nye and Gaitenby 1973, p.90). In sum, in the phonemes system, 

/ә/, /a/, /u/, /aI/, /aυ/, and /oυ/ are not used at all, the phonemes /tʃ/ and /z/ are not 

used in the initial and the phoneme /ʃ/ is not used in the final position (Nye and

Gaitenby, 1973, p.78). In some later versions of MRT word list, the types of missing 

phonemes change but the test still remains unbalanced. 

In addition, it has to be stressed that the PBWT is an open-set test while both the 

MRT and the DRT are closed-set tests. Closed-set tests are preferable to open-set 

tests because they are easier and less expensive to administer and require less 

training, that is, they are less affected by participants’ learning and practice 

(Schmidt-Nielsen 1992). They also result in smaller inter-listener variability of the 
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test results and thus require fewer listeners to achieve the same level of data 

reliability. However, both MRT and DRT tasks focus on one specific phoneme at a 

time while the PBWT focuses attention on the whole word and thus its open-set 

structure is “more likely to match the processing characteristics of the natural 

speech environment” (Greenspan et al. 1998, p. 210). Another advantage of PBWT 

over the MRT and DRT is that its scores are usually lower than the scores of the 

other 2 tests so less possibility exists for the ceiling effect to affect collected data 

in case of testing good communication systems (Schmidt-Nielsen 1992). 

One common drawback of all perceptual speech intelligibility tests is “the 

correlation between mean values and the variance of scores contributing to these 

means” (Studebaker et al. 1995, p. 174). This is also called “the ceiling effect” and 

affects scores on both very low and very high ends of the scale; however, a 

nonlinear transformation of the scores, such as arcsine transformation, can alleviate

the problem (Studebaker 1985; Studebaker et al. 1995). 

5.3 Technical (Predictive) Tests of Speech Intelligibility

The main advantage of all perceptual ratings and intelligibility tests is that because 

these methods are based on the perception of speech by listeners, they do not have 

any limitations in respect to the characteristics of the communication system or 

those of the test environment. However, their common limitation is that they can 

be time-consuming and costly, especially when the tests are conducted within an 

iterative design process. In addition, to verify that the obtained results are reliable, 

the test should be repeated at least once and some measure of repeatability derived 

from the data.

Technical measures of speech intelligibility use synthetic speech-like signals, or the 

relevant average speech and noise spectra, and the technical parameters of the 

communication system to calculate speech intelligibility. They were developed as 

fast and inexpensive substitutes to perceptual measures for assessment of speech 

intelligibility and include real-time and on-line applications. They do not actually 

measure speech intelligibility but predict it on the basis of the transmittability of 

the input signal. Their values typically vary from 1.0 (perfect) to 0 (poor) and they 

are assumed to be univocally related to speech intelligibility scores from 100% to 

0%. Such measures are very useful in the design phase of the communication 

systems to ensure that potential problems with speech intelligibility are identified 

and resolved early. They also are widely used in the case of existing communication 

systems since they are more cost-effective and time-effective than perceptual tests.

However, they do have their own limitations and they are not substitutes for 

perceptual speech intelligibility testing of the operating systems unless they a) have 
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been proven to sufficiently account for all the types of distortions caused by the 

signal processing techniques used in the tested system or b) their relative scores are 

sufficiently higher than the corresponding human tests intelligibility scores 

required for the tested system. When speech intelligibility is predicted by technical 

measures, one needs to consider that speech compression and peak-clipping 

operations may be seen by the technical measures as decreasing SNR and therefore 

resultsin artificially low speech intelligibility predictions, although in fact they may 

enhance speech intelligibility.

The 3 most commonly used quantitative speech intelligibility predictors

standardized in ANSI/ASA S3.5 and IEC 60268-16 are:

1) Articulation Index (AI),

2) Speech Transmission Index (STI), and

3) Speech Intelligibility Index (SII). 

Several other measures, such as articulation loss of consonants (ALcons) (Peutz 

1971), speech interference level (Beranek 1947), or speech audibility index 

(Mueller and Killion 1990), also exist and are included in some comparative 

studies. For example, Barnett (1997) compared speech intelligibility scores 

measured with ALcons with scores obtained with some form of the STI test. 

However, these other methods are either less general in their applications or are less 

widely used. 

In addition to the above predictors of speech intelligibility, there is the group of 

measures (algorithms) recommended by the ITU for prediction of speech quality, 

and more specifically for prediction of the MOS number. The most advanced

among them is the Perceptual Evaluation of Speech Quality (PESQ) algorithm. 

Some of the other algorithms are the Perceptual Analysis and Measurement System

(PAMS) and Perceptual Speech Quality Measurement (PSQM). All of these 

measures calculate the overall amount of signal distortion introduced by the 

transmission system and transform it through a psychoacoustic model to estimate 

the speech quality of the output signal. PESQ scores are reported to be well 

correlated with speech intelligibility ratings under some conditions (R=0.91-0.99) 

(Beerends et al. 2004; 2005; 2009). However, since they are, by definition, 

estimates of speech quality and not speech intelligibility, they are not discussed 

here. 

5.3.1 Articulation Index (AI)

The AI is based on the concept that the overall speech intelligibility of the

communication system results from independent intelligibilities calculated from the 
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peak-speech-to-root-mean-square noise ratio measured in selected frequency bands 

from 200 to 7000 Hz (Fletcher and Steinberg 1929; Fletcher 1953; French and

Steinberg 1947; Kryter 1962ab). Three methods of calculating AI are offered: a) 

critical band (n = 20) method (most sensitive), b) third-octave band method, and c) 

octave band method (easiest). The AI assigns a value between 0 (poor) and 1 

(perfect) to speech intelligibility. An AI score of 0.3 or below is considered 

unsatisfactory, greater than 0.3 to 0.5 is satisfactory, greater than 0.5 to 0.7 is good, 

and greater than 0.7 is very good to excellent.

The AI accounts for system noise and linear distortions and its calculations are 

described in the ANSI S3.5 (ANSI 1969) speech intelligibility standard. It is a 

measure similar to a Shannon’s channel capacity (Shannon 1948) defining the 

maximum amount of information that the system can transmit without error (Allen 

2003). However, the AI does not sufficiently account for the effects of 

reverberation, compression, and nonlinear signal distortions (e.g., Humes et al. 

1986; Knight 1994; Payton et al. 1994) and has since been superseded by SII in the 

newer version of the same standard (ANSI/ASA 2017). 

5.3.2 Speech Transmission Index (STI)

The STI (developed in early 1970s at the Netherlands Organisation for Applied 

Scientific Research (TNO) [Nederlandse Organisatie voor Toegepast

Natuurwetenschappelijk Onderzoek] in the Netherlands) uses a concept similar to 

AI, in that the prediction of speech intelligibility is based on a weighted 

contribution for a number of frequency bands but it also employs the concept of 

modulation transfer functions (MTF) and uses a synthetic speech-like signal based 

on a number of amplitude modulation processes (Steeneken and Houtgast 1980). 

The analysis is performed using 7 octave bands, from 125 to 8000 Hz, and 

14 modulation frequencies. The reduction of the depth of modulation, expressed as 

the MTF, measured at the output of the communication channel is considered to 

represent loss of intelligibility. Speech intelligibility measured on the STI scale is 

considered excellent at and above 0.75, good between 0.75 and 0.6, fair between 

0.6 and 0.45, poor between 0.45 and 0.3, and bad below 0.3.

The STI was originally developed to predict the effects of background noise level 

and room reverberation on the intelligibility of male speech, heard directly or 

passing through a communications channel. In the case of noisy and reverberant 

speech signals it was reported to predict PBWT values with standard deviations of 

about 5.0% (Steeneken and Houtgast 1980; Jacob et al. 1991). However, the 

original STI was shown to severely underestimate speech intelligibility for systems 

with extremely limited bandwidths (e.g., horn loudspeakers for which the 

frequency response starts at 1000 Hz), does not account for nonlinear distortions 
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produced by the transmission system, and underpredicts the intelligibility of female 

voices. Therefore, the original STI was extensively revised (for a summary, see 

Steeneken and Houtgast 2002) and its revised version (STIr) alleviates several of 

the original version’s limitations. The revised STI accounts for some nonlinear and 

time domain distortions (e.g., peak clipping, automatic gain control, presence of 

echoes), implements redundancy correction for adjacent frequency bands (signalto-noise ratios in adjacent bands are no longer considered independent), and 

redefines frequency weighting functions to account for both male and female 

voices. 

The current STI calculation procedures, a direct calculation method based on MTF 

and an indirect calculation method (Schroeder 1981) based on impulse response 

(for comparison see Zhu et al. 2014a), were standardized internationally in the IEC 

60268-16 standard (IEC 2011). However, despite its wide popularity and the steady 

improvement of its algorithms, the STI test is criticized as an unreliable predictor 

when nonlinear processes and compression are involved (Ludvigsen et al. 1993; 

Goldsworthy and Greenberg 2004). In the case of room acoustics, Onaga et al. 

(2001) reported that the STI does not discriminate properly between early reflection 

energy, which contributes to speech intelligibility and reverberant energy and 

echoes, which decrease intelligibility. The STI overestimates speech intelligibility 

in environments that are both reverberant and noisy (Payton et al. 1994). There have 

also been concerns voiced by some practitioners that STI does not account for the 

effects of perceptual masking and fluctuating background noise. For example, van 

Schoonhoven et al. (2017) reported that when the indirect STI calculation method 

is used, a minimum impulse-to-noise ratio of 25 dB in fluctuating noise is needed 

to get meaningful results. Mechergui et al. (2017, p. 1471) regarded both the direct 

and indirect methods as “not adequate for real time intelligibility monitoring”. 

Please note, however, that the STI algorithm has been gradually modified during 

the last 25 years; thus, some of these early criticisms may no longer be true.

Since the STI measure is computationally intensive, several simplified versions of 

STI have been developed to be used for less stringent transmission requirements. 

The Rapid Speech Transmission Index (RASTI), implemented in equipment 

produced by Brüel & Kjær (Denmark) and popular in some European countries, 

limits its calculations to the 2 octave bands centered at 500 and 2000 Hz and a 

limited set of 9 modulation frequencies (4 in the 500 Hz band and 5 in the 2000 Hz 

band). However, the RASTI frequently overestimated intelligibility and has since 

been abandoned. Another simplified version, the Sound Transmission Index Public 

Address (STIPA), uses all 7 STI octave bands but only a subset of 7 modulation 

frequencies (one frequency per band). It requires less computational power and can 

be implemented on simple handheld devices. It is commonly used for measuring 
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the intelligibility of public address (PA) systems in airports and railway stations. 

According to studies conducted by Gomez et al. (2007) and Zhu et al. (2014a) there 

are no substantial differences between the STI and STIPA metrics regardless 

whether the direct or indirect STI measurement method is used. The difference 

between values seldom exceeds 0.03, equivalent to 1 “just noticeable difference” 

(JND) for STI scores (Bradley et al. 1999). 

5.3.3 Speech Intelligibility Index (SII)

The SII, described in the ANSI/ASA S3.5 standard (ANSI/ASA 2017), was 

developed as a combination of the AI measure with some version of MTF that 

accounts for the effects of room reflections. The concept of the SII is based on 

measuring the SNR in a number of frequency bands, and weighting each SNR by 

band-importance functions that are dependent on speech material. Four methods of 

calculating the SII are offered, each using a different number and size of frequency 

bands. In an order of their accuracy, they are: a) critical bands (n = 21), b) one-third 

octave bands (n = 18), c) equally-contributing bands (n = 17), and d) octave band 

(n = 6) variants. However, Zhu et al. (2104b) compared SII scores obtained with 

the octave- and one-third-octave band methods and have found only small 

differences in the resulting scores. In addition, the SII includes corrections for 

speech spectrum changes due to changes in vocal effort and to account for upper 

spread of masking (for more information see ANSI/ASA (2017) and Hornsby 

(2004)). SII calculations have been reported to account for the effects of 

reverberation and stationary noise and correlate well with the results of perceptual 

tests and STI. The score can be adjusted for a person wearing hearing protection 

devices and also for the presence of visual cues. Since the SII is based on the longterm average spectrum of noise, it is a poor predictor of the effects of nonstationary 

noise. Similarly, it does not account well for peak-clipping and other similar 

nonlinear distortions. Several authors proposed various modifications of SII to 

mitigate these limitations, but they were only partially successful (Kates and

Arehart 2005; Rhebergen and Versfeld 2005; Rhebergen et al. 2006).

Unfortunately, the ANSI/ASA standard includes no information about the 

relationship of SII-derived values to intelligibility scores obtained by human 

listeners. However, Lyzenga and Rhebergen (2010) reported that the speech 

recognition threshold corresponds to SII values of approximately 0.22 and to 50% 

sentence recognition. In addition, Bradley (2003) reported that SII values are about 

0.07 higher than AI values calculated for the same speech material and transmission 

conditions when the AI score is below 0.55.

According to Larm and Hongisto (2006, p. 1117), “the SII and STI are so similar, 

when used for room acoustical purposes, that the need for many different quantities 
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23

is questionable.” Similarly, Zhu et al. (2014b) compared SII and STI metrics in 2 

different rooms and at several SNR ratios and found no large differences. However, 

the authors concluded that “relative loose restriction on measurement conditions in 

ANSI/ASA S3.5 may reduce the comparability of different measurements” (Zhu et 

al. 2014b, p. 7). In addition, the SII has been criticized for insufficiently accounting

for nonlinear distortions and upward spread of masking (Robinson and Casali 2000; 

Valimont 2006; Schlesinger and Boone 2010).

In general, technical methods of predicting speech intelligibility are fast and 

convenient to apply; however, all computed measures have some limitations 

dependent on the algorithm implemented. Such measures should be used carefully 

if the communication system uses nonlinear signal processing such as signal 

compression, level limiting, or phase shift. Therefore, the use of these measures 

requires operating personnel with significant experience and analytical skills with 

the ability to identify sources of potentially inaccurate and misleading data. 

MIL-STD-1474E (2015), as well as its predecessor MIL-STD-1474D, states that 

technical measures shall not be used to predict intelligibility of synthetic speech 

because some key acoustic features are not present in non-human “speech”. Instead, 

the standard stipulates that the intelligibility of synthetic speech shall be measured 

using perceptual intelligibility tests. However, it should be noted that Chen and 

Loisou (2011ab) evaluated performance of several speech intelligibility predictors 

in relation to synthetic speech assessment and found that STI prediction, although 

not AI prediction, was highly correlated with listeners’ performance (r = 0.92). This 

may suggest that STI, and potentially SII, may be used for the assessment of 

synthetic speech transmission systems.

All 3 of the technical measures discussed (AI, STI, SII) have also been criticized 

for being too restrictive and for not taking into account the effects of nonstationary 

character of noise on speech communication; however, other available measures do 

account for these effects. For example, the Extended SII algorithm, proposed by 

Rhebergen and colleagues (2005; 2006), provides provisions for nonstationary 

noise and seems to be more suitable than the original SII for the prediction of realworld speech intelligibility. Other measures, such as the Dau Model and Glimpse

Proportion Metric, are intended to handle the effects of nonstationary noise, but 

they have not been yet sufficiently tested (Tang and Cooke 2011). It must be 

stressed that the presence of continuous noise in a communication system 

represents the worst case scenario; thus, in applications where communication is 

critical and a conservative estimate of minimum speech intelligibility is required, 

nonstationary noise algorithms are not appropriate.
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In addition, all perceptual and technical measures of speech intelligibility discussed 

here assume monaural or diotic (the same signal at both ears) speech reception. 

They have been criticized for not taking into account properties of the binaural 

(dichotic) hearing (different signals delivered to each ear) although some binaural 

implementations of perceptual tests have already been tried and some binaural 

extensions of STI (Schlesinger and Boone 2010) and SII (Beutelmann and Brand 

2006) have already been proposed. It has to be noted that binaurally presented

speech typically results in greater intelligibility than diotic speech due to the 

“spatial release from masking” resulting from the spatial separation of the speech 

source from the noise. For example, Pollack and Picket (1958) reported that,

depending on the relative positions of speech and noise sources, stereophonic 

reproduction of speech in noise provided a 5.5 to 12 dB advantage in comparison 

to monophonic reproduction. However, in the case of the communication system,

the monaural or diotic transmission is the worst case scenario and monaural (diotic) 

testing should be used as a conservative estimate for critical systems. 

6. Common Intelligibility Scale

Equipment designers and users are often confused about the relative merits of the 

large number of perceptual and technical speech intelligibility measures described 

in the literature and offered by equipment and software developers. In addition, 

existing industry and government standards frequently refer to different criteria and 

test methods, resulting in multiple measurements taken using different scales and 

tests. If speech intelligibility metrics for 2 different communication systems were 

obtained using different intelligibility tests, the systems cannot be directly 

compared unless there is an established relationship between those test scores. 

Therefore, to establish relationships between the scores obtained with different tests 

and measures, several authors have proposed or derived graphical or numerical 

relationships based on the results of studies using 2 (or more) speech intelligibility 

measures. Some of these relationships have been developed on the basis of a 

common physical measure (e.g., SNR). Unfortunately, such an approach is very 

limited in its generalizability since the observed relationships are not transferable. 

For example, 2 tests may differ in their relationship at a specific SNR if the speech 

or noise characteristic changes and the same tests may result in different score 

difference for specific speech and noise conditions if the SNR changes. Thus, the 

relational functions developed to facilitate comparison of various tests and allow 

conversion of test scores obtained under specific physical test conditions cannot be 

translated to other test conditions and test materials.
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25

To facilitate the general comparison of scores obtained with different intelligibility 

tests under different test conditions, a common denominator is needed. One such 

common denominator, the Common Intelligibility Scale (CIS), was proposed in the 

mid-1990s by Barnett and Knight (Barnett and Knight 1994; Knight 1994; Barnett 

1999). The CIS corresponds to STI as follows, CIS = 1 + log (STI). The authors 

took STI and 6 other common speech intelligibility tests (measures) and using 

established relationships between them that were published, among others, by 

Kryter (1970, p. 76) and Steeneken and Houtgast (1980), and expressed them all on 

a common abstract scale, which they named the CIS. The relationships between 

these 7 scales expressed as a function of the CIS are shown in Fig. 4. The concept 

of the CIS is that if under some test conditions a specific intelligibility test results 

in a given score, the scores for other tests conducted under the same test conditions 

can be predicted by looking at their values at the same CIS coordinate. In addition, 

knowing the relationship between one of the speech intelligibility tests plotted in 

Fig. 4 and a new test is sufficient to predict the relationship of this new test with 

any of the other tests plotted already in the graph. 

Fig. 4 Relationships between various speech intelligibility scales expressed on the common 

intelligibility scale (Knight 1994)
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26

According to Knight (1994, p.64), the CIS “was designed to make the chart both 

readable and usable and renders the word score data as straight lines indicating a 

linear relationship between the CIS and perceived intelligibility”. Thus, the CIS 

assumes a linear relationship with the true perceived intelligibility and monotonic 

nonlinear relation with all test scales represented in Fig. 4. An example of this

nonlinear relationship can be observed in the relationship between CIS and STI, 

which can be expressed mathematically as

CC = 1 + log(SS ), (4)

Knight (1994) warns, however, that where the gradients of curves representing 

specific tests are shallow, the resulting CIS values are not reliable (Knight 1994). 

Therefore, while CIS may allow an approximate prediction of one score on the basis 

of another, it cannot be considered an accurate translation procedure in all cases. 

Further research is needed to improve accuracy and reliability of translations.

In using CIS it should be remembered that most of the data used for deriving the 

functions shown in Fig. 4 were based on speech-in-noise experiments since noise 

is the main factor affecting speech intelligibility. More studies with focus on the 

effects of reverberation and nonlinear speech processing are needed to either finetune the existing curves or to derive alternative sets of curves for various 

environments. Such studies may help to account for some discrepancies between 

STI and AI (SII) functions shown in Fig. 4 and results of some indirect comparisons 

between these measures obtained in complex environments (e.g., Payton et al. 

1994; Larm and Hongisto 2006; Foster 2015). 

More research also needs to be done to establish unique relationships between 

various test scores for specific languages and to determine whether determined 

relationships vary as functions of specific speech distortions and listening 

conditions (Williams and Hecker 1968). For example, Galburn and Kitapci (2016) 

examined the impact of room acoustics on speech intelligibility of 4 languages 

(English, Polish, Arabic and Mandarin) and observed large differences among 

languages in results of both listening and predictive speech intelligibility tests. 

“English was the most intelligible language under all conditions, and differences 

with other languages were larger when conditions were poor” (p. 79). Polish and 

Arabic were particularly sensitive to room conditions.
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27

7. Selecting a Speech Intelligibility Test and Interpreting Its 

Score

The curves shown in Fig. 4 may guide one in the selection of a speech intelligibility 

test for a specific application. First, all other things being equal, the ideal test is the 

one with the greatest discrimination power (i.e., a steep gradient and a small 

standard deviation) along the CIS in the range of intelligibility being investigated. 

Thus, according to Fig. 4, the STI offers very good discrimination at high values of 

speech intelligibility (CIS > 0.8) while the ALcons and PBWT (1000 or 256 words) 

scores have good discrimination at low speech intelligibility (CIS <0.5). In 

selecting a test for a particular application one should also consider the degree to 

which the speech materials correspond to the context in which communication will 

occur, as well as whether reference scores obtained with similar systems are 

available. The target words should be embedded in a carrier phrase when perceptual 

assessments of speech intelligibility involve room acoustics to capture the effects 

of reflections and reverberation (IEC 1998). 

Finally, it is useful to interpret the intelligibility score in terms of qualitative 

categories such as good or bad. Houtgast and Steeneken (1984; 1985) proposed a 

system of 5 qualitative ranges of speech intelligibility—

Excellent/Good/Fair/Poor/Bad—and assigned the STI values to each of them (see 

also Steeneken and Houtgast 2002). Later on, Knight (1994) assigned the CIS 

values to these 5 categories. However, these values disagree with the CIS values 

calculated according to Eq. 4 expressing the relationship between CIS and STI. 

Table 4 shows the qualitative scale described above with the CIS and STI values 

assigned to them. 

Table 4 Ranges of speech intelligibility and corresponding values on the STI and CIS scales 

Speech intelligibility STI (Houtgast and Steeneken 1984) CIS (Knight 1994)

STI CIS = 1+log(STI) 

Excellent > 0.75 > 0.88 > 0.90

Good 0.60–0.75 0.78–0.88 0.70–0.90

Fair 0.45–0.60 0.65–0.78 0.60–0.70

Poor 0.30–0.44 0.48–0.65 0.50–0.60

Bad < 0.30 < 0.48 < 0.50

The differences between the 2 CIS values given in Table 4 are minimal at the ends 

of the scale but larger in the middle. When CIS (Knight 1994) values are converted 

to STI, the differences between these values and the values listed by Houtgast and 

Steeneken are larger than 0.03 and exceed the uncertainty of STI instruments 
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28

(STI=0.02-0.03) (Wijngaarden and Verhave 2002; 2006) and the JND value for the 

STI scores (Bradley et al. 1999). The greatest difference is for Good/Fair boundary 

and exceeds 3 JNDs.

Since the relationship expressed in Eq. 4 was the foundation of the CIS, the CIS 

values assigned to each qualitative range ought to be based on this function, or 

conversely, a new set of STI ranges should be derived from the Knight’s (1994) 

proposal. A review of literature (e.g., IEC 1998; NFPA 2010) indicates that the 

former approach is commonly accepted. This relationship is shown graphically in 

Fig. 5.

Fig. 5 CIS and STI values defining STI categories proposed by Houtgast and Steeneken 

(1984; 1985)

Note that the qualitative scale shown in Table 4 and Fig. 5 is actually a MOS scale 

with references to the CIS (Table 4) and STI scales. This relationship is shown in 

Fig. 6.

Fig. 6 CIS and STI potential relation with category rating (CCR) scale used as the MOS 

scale in telephone networks quality evaluation

On the basis of the framework shown in Fig. 6, all other intelligibility scales 

included in Fig. 4 can be added to the Fig. 6 to show qualitative categories along 

their extent. New scales can be added in the future. This approach allows one to 

select a speech intelligibility test for a particular application on the basis of its 

appropriateness for a given environment, potential speech distortions, test 

availability (instrumentation, listeners), experience of the test personnel and 

external restrictions (time, budget) rather than on the basis of past history of tests 

in similar environments and limited literature references. 
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29

The final decision when using a speech intelligibility test for a particular application 

is the choice of a threshold value of speech intelligibility for a pass/fail decision in 

determining the “health” of a given environment (instrumentation, human being) 

from a speech intelligibility point of view. Alternatively, several such threshold 

values may be needed to classify a given environment to one of the predetermined 

quality classes or categories. 

A commonly recommended criterion for spoken warning alerts in public spaces is 

a minimum speech intelligibility corresponding to the STI score 0.5 (0.7 CIS). If 

speech intelligibility varies across the listening area, it is recommended that the 

average STI score minus one standard deviation be 0.5 STI (0.7 CIS) or greater and 

at least 90% of the area have STI values of not less than 0.45 (0.65 CIS) (IEC 1998; 

NFPA 2010). The threshold STI value of 0.5 is also recommended in the ISO 7240

standard, replacing IEC 60849†

. This standard also lists mean and minimal criterion 

values for speech intelligibility as measured for other speech tests. These values are 

shown in Table 5 and all of them correspond to 0.7 CIS (Fig. 4).

Table 5 Required speech intelligibility scores as per ISO 2470:2007 (ISO 2007b)

Measurement method Required intelligibility score

Mean intelligibility Minimum intelligibility

STI or STIPA 0.50 0.45

PB 256 words (%) 94 91

PB 1000 words (%) 77 68

MRT (%) 94 90

SII 0.50 0.45

Note: Minimum Intelligibility applies to a single location within the space.

It seems like the “mean” specifications in Table 5 represent the normal acceptable 

speech intelligibility for public communication. Some applications, such as military 

communication, air traffic control or “red telephone lines,” may require better 

speech intelligibility to avoid human losses, international conflicts, or public 

confusion. Therefore, current Federal Aviation Administration (FAA) standard HFSTD-001B (Ahlstrom 2016) and the US military standard MIL-STD-1472 (DOD 

1999b; 2015) list values for High, Normal, and Minimal speech intelligibility 

criteria to be used depending on the criticality of the application. These standards 

recommend use of the PB, MRT, and AI tests for measuring speech intelligibility 

and do not provide references to either the CIS or STI scales. A summary of the 

proposed values is given in Table 6.

 † The CIS value is no longer listed in ISO 7240 (2007ab).
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30

Table 6 Intelligibility criteria for voice communication systems

Communication 

requirement

Required intelligibility score

PBWT 1000 MRT AI

High 90 97 0.7

Mean (Normal) 75 91 0.5

Minimal 43 75 0.3

A comparison of Table 6 and Fig. 4 shows that the values of PBWT 1000 (1000 

words) and AI scores listed in Table 6 for the High, Normal, and Minimal 

intelligibility thresholds agree with 0.8 CIS, 0.7 CIS, and 0.5 CIS values, 

respectively, for the respective test curves shown in Fig. 4. This agreement serves 

as a basis for establishing the CIS values for High, Normal, and Minimal threshold 

criteria from the other speech tests shown in Fig. 4 as well as for adding MRT 

values to the CIS and Fig. 4. 

It is tempting to expand Fig. 4 to include other common speech intelligibility tests 

but it creates a chart that is difficult to follow. Therefore, it seems useful to create 

an expanded table of recommended High, Mean, and Minimal speech intelligibility 

threshold scores for many common speech intelligibility measurement scales. 

Table 7 presents a proposed table created from the data in Figs. 4–6, Tables 4–6,

and some additional literature (e.g, Ariöz and Günel 2016).

Table 7 Intelligibility score requirements as a function of communication criticality

Communication 

requirement

Common 

intelligibility 

scale (CIS)

Speech 

intelligibility tests

Predictors of speech 

intelligibility

MRT PBWT AI STI SII

High intelligibility; separate 

syllables understood;

MOS ≥ 4

0.78 97% 90% 0.70 0.62 0.72

Normal intelligibility; about 

98% of sentences correctly 

heard; digits understood; 

MOS ≥ 3.5

0.70 91% 77% 0.50 0.504 0.57

Minimal intelligibility;

about 90% of sentences 

correctly heard; standardized 

phrases understood;

MOS ≥ 2.2

0.50 75% 43% 0.30 0.32 0.37

Test values for SII have been added to those of the CIS, MRT, PBWT, STI, and AI, 

as SII replaces the AI in the ANSI/ASA standard. The values shown were 
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31

determined from several literature studies that compare these tests with others (e.g., 

Bradley 2003; 2004). 

The values provided in Table 7 apply to contexts in which speech comprehension 

is critical to the user. In contrast, speech privacy is an important design criterion for 

open offices; therefore, unwanted speech, if still audible, should have low 

intelligibility, with AI, SII, and STI scores no greater than 0.15, 0.20, and 0.20, 

respectively (Bradley 2003; Pop and Rindel 2005). 

8. Discussion and Conclusions

The transmission of speech within a specific acoustical context or over a 

communications system depends on the fidelity of the signal reaching the listener. 

Factors that affect this fidelity include characteristics of the transmitted speech 

signal itself, the noise masking within the communication medium, and the effects 

of room acoustics and electronic distortions. The measurement of transmission 

effectiveness depends on the objective, whether it be to describe its perceived 

quality, or to assess its intelligibility. Speech quality assessments can be somewhat 

subjective, whereas intelligibility is more easily quantified as the percentage of 

speech material recognized. These 2 speech parameters are frequently, but not 

always, highly correlated. For example, peak-clipping of speech has a detrimental 

effect on speech quality but it does not affect, and can even slightly improve, speech 

intelligibility. Therefore, the specific objective, quality or intelligibility, should be 

clearly stated and communicated to evaluators using perceptual scaling. It is not 

uncommon to find evaluations for which the intended measure was speech 

intelligibility, but the resulting assessment only gives estimates of speech quality. 

In the case of speech intelligibility scoring, the percentage of speech material 

properly recognized is highly dependent on the complexity and predictability of the 

speech materials used, whether they are phonemes, syllables, words, or sentences. 

Further, the test difficulty depends on the size of the test vocabulary and whether 

the response set is open or closed. Although perceptual measures of speech 

intelligibility such as the MRT and PBWT are preferred, because they allow one to 

directly measure the effects of nonlinear factors on human speech recognition, they 

are costly in terms of the time and number of listeners required to evaluate a 

communications system. Technical measures such as AI, STI, and SII provide 

quick, inexpensive methods for evaluating speech intelligibility that are based on 

measuring the changes to signal input as a function of the communications medium; 

however, these are only more or less successful in accounting for temporal factors 

such as reverberation and distortion. It is suggested that persons measuring speech 

intelligibility use a common intelligibility scale that cross-references speech 
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32

intelligibility scores obtained within the same communication medium for several 

standard measures. The CIS may not allow a precise translation of speech 

intelligibility scores from one test to another; however, it does allow one to estimate 

their approximate relationship. Further, by providing information about each test’s 

relative sensitivity as a function of the test acoustical environment, the CIS may 

serve as a tool that enables the user to select the optimal test for a given context. 

To assess the utility of the CIS, let us consider the following example. There are 2 

ANSI/ASA standards that specify methods for measuring speech intelligibility, 

ANSI/ASA S3.2 (2014; perceptual) and ANSI/ASA S3.5 (2017; predictive). One 

of the criticisms of ANSI/ASA S3.2 is that the 3 speech sets it specifies do not 

correspond well to the vocabularies used in operational environments. Currently, 

the speech intelligibility measure most commonly used by the military is the MRT, 

and its common use makes it possible to compare current measures with previously 

made assessments. If the test and evaluation process incorporates the use of 

untrained, but otherwise representative listeners, such as using Soldiers for 

evaluations of tactical communications equipment, the use of speech materials 

containing obscure terminology may result in frustration and a sense that the testing 

lacks relevancy. Alternative speech intelligibility measures, such as the Callsign 

Acquisition Test (Rao and Letowski 2006) have been developed with the aim of 

assessing speech intelligibility using materials that are representative of military 

communications. ANSI/ASA S3.2 allows for the use of alternative speech 

materials; however, their use may limit the ability to compare the resulting data to 

previous assessments. By using the established relationship of these measures to a 

standardized measure such as the MRT and the CIS, data from these alternative 

measures can be interpreted and approximate equivalencies posited (Blue-Terry 

and Letowski 2011; Blue-Terry et. al 2012; Figs. 7 and 8). Since communications 

systems are often chosen on the basis of many factors, only one of which is speech 

intelligibility, these alternative measures may have a valid role to establish 

sufficient performance for use, or to screen among systems under consideration. 

Thus the ability to relate these scores to more widely used measures can serve as a 

practical cost saving measure. 
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33

Fig. 7 Performance intensity functions obtained for the callsign acquisition test (CAT) and 

the MRT in white noise (Blue-Terry and Letowski 2011)

Fig. 8 Theoretical shapes of MRT and CAT performance intensity functions derived for 

data from the CAT and MRT speech intelligibility measures (Blue-Terry et al. 2012)

Additionally, and importantly, the utility of the CIS is that it frequently allows one 

to compare results of 2 tests with no need to refer to the test conditions. For 

example, comparing 2 speech intelligibility tests in 2 different types of noise will 

most likely result in a different relationship between these tests as a function of 

SNR for each type of noise. Replacing the SNR with CIS allows making this 

relationship unique and independent of noise type. With respect to the CIS, the 

difference “X” between 2 tests of speech intelligibility will always be the same,
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34

although they may refer to different SNRs due to characteristics of the noise 

masker. It does not mean that the CIS should serve as a replacement of SNR when 

presenting data, but it provides a context for the data that may enable decisionmaking in some cases.

Alternative measures, such as CAT, have a valid role in establishing adequate 

intelligibility for use, or to screen among systems under consideration. Thus the 

ability to relate their scores to more widely used measures, and to do this in a 

generalized form, can serve as a practical cost saving measure and may enable good 

decisions. 
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35

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List of Symbols, Abbreviations, and Acronyms

AI articulation index

ALcons articulation loss of consonants

ANSI American National Standards Institute

ASA Acoustical Society of America

CAT callsign acquisition test

CCR comparison category rating

CID Central Institute for the Deaf

CIS common intelligibility scale

CQS conversational-quality scale

CVC consonant-vowel-consonant

dB decibel

dB(A) A-weighted decibels

DCR degradation category rating

DOD Department of Defense

DRT diagnostic rhyme test

FAA Federal Aviation Administration 

INT intelligibility 

ITU International Telecommunication Union

JND just noticeable difference

LES listening-effort scale

LQS listening-quality scale

LPS loudness-preference scale

MOS mean opinion score

MOS-LQS MOS-listening-quality scale

MRT modified rhyme test
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MTF modulation transfer function

PA public address

PAMS perceptual analysis and measurement system

PB phonetically balanced

PBWT phonetically balanced word test

PESQ perceptual evaluation of speech quality

PQS picture-quality scale

PSQM perceptual speech quality measurement

QoS quality of service

RASTI rapid speech transmission index

SD standard deviation

SII speech intelligibility index

SIR speech intelligibility rating

SIS speech intelligibility scale

SNR speech-to-noise ratio/signal-to-noise ratio

STI speech transmission index

STIPA sound transmission index public address 

STIr speech transmission index-revised

TNO Netherlands Organisation for Applied Scientific Research 

(Nederlandse Organisatie voor Toegepast

Natuurwetenschappelijk Onderzoek)

WDRC wide dynamic range compression

VoIP voice over internet protocol
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