Jitter and Shimmer: What They Really Tell You (And What They Don't)
🎯 Key Takeaways
- Jitter and shimmer aren't obsolete—but they have critical limitations you must understand
- ASHA now recommends CPP as primary measure—jitter/shimmer are supplementary
- They FAIL for severe dysphonia—when you need them most, they become invalid
- Values above 5% are unreliable—the algorithm can't identify vocal cycles properly
- Praat and MDVP give different values—never compare across software or use wrong norms
If you trained as an SLP anytime before 2018, you probably learned that jitter and shimmer were the gold standard for objective voice assessment. These measures appeared in every textbook, every voice lab report, every research paper. They felt scientific—precise numbers that could quantify what we heard perceptually.
Then the 2018 ASHA Expert Panel published their recommendations, and everything shifted. CPP (Cepstral Peak Prominence) became the recommended primary measure, and jitter/shimmer were demoted to supplementary status. Many clinicians were left confused: Were all those years of jitter and shimmer measurements meaningless?
The answer is nuanced. Jitter and shimmer remain valuable tools—but only when you understand their limitations. This article explains exactly when these traditional measures work, when they fail, and how to use them appropriately in modern clinical practice.
What Jitter and Shimmer Actually Measure
Let's start with the basics, because the underlying concept matters for understanding the limitations.
Jitter
Cycle-to-cycle variation in pitch period.
Measures how consistent the time between vocal fold vibrations is. A perfectly periodic voice would have zero jitter. Higher jitter = more irregular pitch from one cycle to the next.
Clinical association: Roughness, hoarseness, neurological voice disorders
Shimmer
Cycle-to-cycle variation in amplitude.
Measures how consistent the loudness is from one vocal fold vibration to the next. Higher shimmer = more irregular amplitude, often perceived as breathiness or weakness.
Clinical association: Breathiness, reduced loudness, glottal insufficiency
Here's the critical insight: both measures require the software to identify individual vocal fold cycles. The algorithm must find the boundary between each glottal pulse to calculate how much they vary. This works beautifully for relatively normal voices—and fails catastrophically for severely disordered ones.
The Fundamental Problem: When Algorithms Fail
Ingo Titze, one of the most influential voice scientists, developed a signal classification system that explains exactly when perturbation measures become invalid:
Clear harmonic structure. Algorithm can identify cycles reliably. Jitter and shimmer are VALID.
Strong subharmonics approaching F0 energy. Algorithm may misidentify cycles. Use with CAUTION—may be invalid.
Chaotic signal with no reliable periodicity. Algorithm cannot identify cycles. Jitter and shimmer are INVALID.
Stochastic noise dominates. No periodic structure to analyze. Jitter and shimmer are MEANINGLESS.
The Clinical Catch-22
Here's the frustrating reality: jitter and shimmer become invalid precisely when you need objective measures most—in severe dysphonia. Your patient with the most obviously disordered voice? The algorithm can't reliably measure it. Your patient with a mild voice change that's hard to characterize? The algorithm works perfectly.
Titze established a practical threshold: perturbation values exceeding ~5% are unreliable because cycle identification fails. If your software reports jitter of 8% or shimmer of 12%, those numbers may be artifacts of algorithmic failure, not true measures of vocal instability.
Why ASHA Changed the Guidelines in 2018
The 2018 ASHA Expert Panel (Patel et al.) formally recommended CPP as the primary acoustic measure, citing three fundamental limitations of perturbation measures:
- 1
Only valid for sustained vowels
Jitter and shimmer cannot be extracted from connected speech—but connected speech is how patients actually communicate. A voice may sound terrible in conversation but appear "normal" on a sustained /a/.
- 2
Require reliable F0 computation
Which fails in moderate-to-severe dysphonia. The more disordered the voice, the less reliable the measurement—exactly backwards from what we need clinically.
- 3
Weak correlation with perception
Studies show conflicting and often weak correlations between jitter/shimmer and auditory-perceptual ratings. The numbers don't reliably match what clinicians hear.
Why CPP Works Better
CPP doesn't require cycle identification—it measures the overall prominence of harmonic structure relative to noise. This means it works across the entire severity spectrum and for connected speech. Murton et al. (2020) found CPP achieved AUC values of 0.91-0.98 across speech tasks, with sensitivity of 95% and specificity of 90%. That's dramatically better than traditional perturbation measures.
When Jitter and Shimmer ARE Still Valid
Despite their limitations, jitter and shimmer retain genuine clinical value in specific contexts. The research supports continued use when:
Mild-to-moderate dysphonia (Type 1 signals)
When spectrogram shows clear harmonic structure, perturbation measures are valid and can provide useful supplementary information about cycle-to-cycle stability.
Sustained vowel tasks at controlled intensity
Research shows measurements are most reliable at phonation intensity ≥80 dB SPL. Softer phonation produces artificially elevated values even in healthy voices.
Values below 5%
Higher values suggest the algorithm is struggling with cycle identification. If jitter is 1.2% or shimmer is 4%, those are likely valid. If jitter is 9%, be skeptical.
As components within multiparametric indices
AVQI includes shimmer; DSI includes jitter. Within these validated composite measures, perturbation parameters contribute meaningfully alongside cepstral measures.
Specific populations with evidence
Parkinson's disease, presbyphonia, vocal fold polyps, early laryngeal carcinoma, and muscle tension dysphonia all show validated discrimination using jitter/shimmer.
Population-Specific Evidence
Praat vs. MDVP: Why Software Matters
Here's something many clinicians don't realize: Praat and MDVP produce systematically different values for the same voice sample. This isn't a bug—it's a fundamental difference in how the algorithms work.
| Feature | Praat | MDVP |
|---|---|---|
| Period detection method | Waveform matching (cross-correlation) | Peak picking |
| Noise sensitivity | Low | Higher |
| Typical jitter values | Lower | Higher |
| Cost | Free | $3,000+ |
The Dramatic Difference
Boersma (2009) demonstrated this with a simple test: add just 1% white noiseto a perfectly constant-period signal. Praat reports jitter of 0.02% (correctly recognizing it as nearly periodic). MDVP reports jitter of 0.6%—close to pathological threshold—from the exact same signal.
Bottom line: Never compare Praat values to MDVP norms, or vice versa. Never combine results from different software in the same report.
Software-Specific Normative Values
MDVP Thresholds
Praat Typical Values (Healthy)
The Hidden Variable: Vocal Intensity
Perhaps the most underappreciated confound in perturbation measurement is sound pressure level (SPL). Brockmann-Bauser et al. (2018) demonstrated that increasing voice intensity correlates significantly with decreased jitter and shimmer—in both patients AND healthy controls.
Why This Matters Clinically
Without SPL control, a patient's pathology might be masked by louder phonation—they sound disordered but the numbers look normal because they phonated loudly. Conversely, a healthy voice might appear pathological at softer intensities.
Clinical recommendation: Request phonation at comfortable loudness, ideally ≥80 dB SPL. If you can't measure SPL, at least document "comfortable loudness" vs. "soft" vs. "loud" phonation and maintain consistency across sessions.
Which Jitter and Shimmer Variants Are Most Reliable?
If you've ever been confused by the alphabet soup of jitter variants (local, RAP, PPQ5) and shimmer variants (local, APQ3, APQ5, APQ11), here's the practical guidance:
| Variant | What It Does | Reliability |
|---|---|---|
| Local Jitter | Compares adjacent cycles only | Moderate |
| RAP (Relative Average Perturbation) | Smooths over 3 periods | Higher |
| PPQ5 (5-point Period Perturbation) | Smooths over 5 periods | Higher |
| Local Shimmer | Compares adjacent cycles only | Moderate (noise-sensitive) |
| APQ5 | Smooths over 5 periods | Good balance |
| APQ11 | Smooths over 11 periods (MDVP default) | Higher (most smoothed) |
Practical Recommendation
Smoothed variants (RAP, PPQ5 for jitter; APQ5, APQ11 for shimmer) are more reliablebecause they reduce artifacts from period-defining errors. Local variants are more sensitive to subtle changes but also more sensitive to noise. For clinical reporting, consider using both: smoothed variants for primary interpretation, local variants as supplementary information.
Evidence-Based Protocol for Valid Measurements
If you're going to use jitter and shimmer, follow this protocol to maximize validity:
- 1
Check signal type FIRST
View a narrowband spectrogram. Is there clear harmonic structure (Type 1)? If the signal is chaotic or noise-dominated, perturbation measures are invalid—stop here and use CPP only.
- 2
Use sustained vowel /a/
3-5 seconds, comfortable pitch and loudness (≥80 dB if possible). Connected speech cannot be analyzed with perturbation measures.
- 3
Exclude problem segments
Remove voice breaks, diplophonic segments, and onset/offset portions. Analyze only the stable middle portion of the vowel.
- 4
Be skeptical of high values
If jitter exceeds ~5% or shimmer exceeds ~10%, the algorithm may be failing. Report these values with appropriate caveats or rely on CPP instead.
- 5
Use software-specific norms
Document which software and version you used. Never compare Praat values to MDVP cutoffs.
- 6
Always report CPP alongside perturbation measures
CPP is valid across the severity spectrum and for connected speech. It should be your primary measure, with jitter/shimmer providing supplementary information.
Common Questions
Q: Should I stop using jitter and shimmer entirely?
No. The evidence supports continued use for mild-to-moderate dysphonia, specific populations (PD, presbyphonia, MTD), and as components of validated indices like AVQI. The key is understanding when they're valid and when they're not. Use CPP as your primary measure, and add jitter/shimmer as supplementary information when the signal type permits.
Q: My patient's voice sounds terrible but jitter/shimmer are normal. What's happening?
Several possibilities: (1) The voice is too disordered for valid measurement—the algorithm is returning nonsense values. (2) The pathology affects connected speech more than sustained vowels. (3) The disorder involves aspects not captured by perturbation (e.g., strain, tremor, breathiness). This is exactly why CPP on connected speech is now preferred—it captures what perturbation measures miss.
Q: I've been using MDVP cutoffs with Praat. Is that a problem?
Yes, unfortunately. Praat typically produces lower values than MDVP for the same voice. Using MDVP thresholds with Praat data means you're likely missing pathology (false negatives). Look for Praat-specific normative data, or use Praat's values as relative measures rather than applying absolute cutoffs.
Q: How do I explain this to referring physicians who expect jitter/shimmer?
Frame it as evolution, not abandonment. You might say: "Per current ASHA guidelines, we now use CPP as our primary acoustic measure because it's valid across the severity spectrum. Jitter and shimmer provide supplementary information for appropriate cases. CPP of 8.2 dB indicates moderate dysphonia (typical healthy voice: > 14 dB)."
Q: Does PhonaLab report jitter and shimmer?
Yes, along with CPP and all standard acoustic parameters. We display the full picture so you can make informed clinical decisions. Our analysis uses Praat-based algorithms, so values are comparable to Praat (not MDVP). We highlight CPP as the primary measure while providing perturbation values as supplementary information.
Bottom Line: A Balanced Perspective
- 1Jitter and shimmer aren't obsolete—but they're no longer primary measures
- 2CPP should be your primary acoustic measure—valid across severity spectrum and for connected speech
- 3Perturbation measures fail when you need them most—severe dysphonia, aperiodic voices
- 4Values above 5% are suspect—likely algorithmic failure, not true measurement
- 5Software matters—never mix Praat values with MDVP norms
- 6Control intensity—louder phonation artificially lowers perturbation values
- 7Multiparametric indices (AVQI) rehabilitate these measures—shimmer contributes meaningfully within validated composites
🔬 Get the Complete Acoustic Picture
PhonaLab calculates CPP (primary measure), jitter, shimmer, HNR, F0, and AVQI—all in one analysis. See which parameters are valid for your recording and get AI-powered clinical interpretation that considers the full context.
Try Free Voice Analyzer →Praat-based algorithms • CPP + perturbation measures • AVQI multiparametric index
⚠️ Clinical Documentation Tool
The information in this article is provided for educational purposes and clinical workflow support. Acoustic measures should be interpreted within the context of comprehensive voice evaluation including perceptual assessment and patient history. Parameter validity depends on signal characteristics and recording conditions. All clinical decisions should be made by qualified healthcare professionals.
References & Further Reading
- Patel RR, Awan SN, Barkmeier-Kraemer J, et al. (2018). Recommended Protocols for Instrumental Assessment of Voice: American Speech-Language-Hearing Association Expert Panel. American Journal of Speech-Language Pathology, 27(3), 887-905.
- Murton O, Hillman R, Mehta D. (2020). Cepstral Peak Prominence Values for Clinical Voice Evaluation. American Journal of Speech-Language Pathology, 29(3), 1596-1607.
- Brockmann-Bauser M, Bohlender JE, Mehta DD. (2018). Acoustic Perturbation Measures Improve with Increasing Vocal Intensity in Individuals With and Without Voice Disorders. Journal of Voice, 32(2), 162-168.
- Titze IR. (1995). Workshop on Acoustic Voice Analysis: Summary Statement. National Center for Voice and Speech.
- Boersma P. (2009). Should jitter be measured by peak picking or by waveform matching? Folia Phoniatrica et Logopaedica, 61(5), 305-308.
- Amir O, Wolf M, Amir N. (2009). A clinical comparison between two acoustic analysis softwares: MDVP and Praat. Biomedical Signal Processing and Control, 4(3), 202-205.
Dr. Jorge C. Lucero
Professor of Computer Science, University of Brasília
Dr. Lucero has 30+ years researching voice production and vocal fold dynamics. He believes clinicians deserve honest, nuanced guidance about acoustic measures—not oversimplified "use this, ignore that" recommendations. PhonaLab provides the full picture so you can make informed clinical decisions.