Temporal Span of Tone Cues

• Temporal Span of Tone Cues is the duration over which the auditory system and models integrate acoustic features like pitch, prosody, and lexical tone using context-driven mechanisms.
• It encompasses key empirical metrics, such as a ~35 ms integration window for low-frequency phase cues and language-specific windows (100–180 ms) for lexical tone discrimination.
• Insights from this topic inform both auditory perception models and computational speech systems by linking neurobiological data with machine learning performance on tonal language tasks.

The temporal span of tone cues refers to the characteristic time window over which the auditory system, speech-processing algorithms, or artificial neural models extract, integrate, and make use of acoustic features relevant to pitch, prosody, and tonal identity. This span varies as a function of auditory mechanism (e.g., phase locking, interaural coherence), linguistic context (e.g., lexical tone vs. intonation), signal statistics, and the objectives of downstream processing. Recent work delineates both the perceptual limits and neurocomputational correlates of this phenomenon across human and machine models.

1. Perceptual and Acoustic Basis of Temporal Span

Empirical studies define the temporal span of tone cues as the duration in which critical acoustic information for pitch, intonation, or lexical tone discrimination is present and usable. For lexical tone languages, this typically encompasses the interval over which the fundamental frequency (F0) contour and related voice quality features (such as phonation type) are realized on tone-bearing units—generally syllables of 100–200 ms duration, varying by language and tone type (Kim et al., 15 Nov 2025). In psychoacoustic paradigms, the temporal window of integration for tonal discrimination is commonly measured either by limiting the duration of tone cues or by introducing controlled disruptions to temporal fine-structure cues in synthetic stimuli (Reichenbach et al., 2012).

For low-frequency pure tones, temporal fine structure (phase-locked neural activity) is preserved for only a limited window. Reichenbach and Hudspeth find that frequency discrimination thresholds degrade for stimuli in which phase information is changed more frequently than approximately every 35 ms, defining an effective lower integration bound at this time scale for phase-sensitive cues (Reichenbach et al., 2012). At higher frequencies (>3 kHz), central phase locking is absent, and temporal span becomes irrelevant for discrimination since only spectral place cues remain effective.

Binaural phenomena, such as the detection of antiphasic tones in masking noise (N₀Sπ conditions), demonstrate integration over another characteristic window: the perceptual benefit of interaural phase cues—the binaural masking level difference (BMLD)—decays with growing interaural time delay, but is sustained for delays up to ~10–15 ms, reflecting the temporal coherence imposed by peripheral auditory filters (Dietz et al., 2021).

2. Experimental and Computational Quantification

Temporal span is operationalized by evaluating performance as a function of analysis window length in behavioral, acoustic, and machine learning probes.

Behavioral Methods: Psychoacoustic adaptive procedures manipulate the duration or phase continuity of tonal stimuli and measure the minimal resolvable frequency difference (∆f/f) or detection thresholds as a function of cue window. For example, as the duration between phase changes in a tone increases from 14 ms to 400 ms at 500 Hz, discrimination performance improves exponentially towards an asymptotic value associated with place coding alone; the fitted integration time constant is τ ≈ 35 ms (Reichenbach et al., 2012).

Acoustic Feature Windows: In computational linguistics and SSL model analysis, windowed spectro-temporal features (e.g., log-Mel frames) are used to classify tone labels with varying window durations; macro-F1 scores peak at language-specific optimal spans, such as 100 ms for Burmese and Thai and 180 ms for Lao and Vietnamese, with performance declining if windows are too short or overly long (Kim et al., 15 Nov 2025).

Neural and Binaural Models: Minimalist binaural models quantify the temporal integration of interaural phase difference (IPD) cues by monaural filtering (e.g., gammatone filter with ERB ≈ 79 Hz), yielding an effective temporal window of τ_c ≈ 1/ERB ≈ 12.7 ms for integration of binaural phase information (Dietz et al., 2021).

Amplitude Envelope Modulation Spectrum (AEMS): Prosodic analysis via AEMS identifies spectral peaks in the energy of the amplitude envelope at multiple timescales—syllable (∼200 ms), foot (∼500 ms), phrase (∼1–3 s), and discourse (>3 s)—demonstrating the multi-level temporal distribution of tone cues in natural speech (Gibbon, 2018).

3. Mechanisms and Theoretical Interpretations

The mechanisms setting the temporal integration window for tone cues are modality- and task-dependent.

Phase Locking and Fine Structure: Temporal fine structure cues from phase-locked spiking in the auditory periphery are exploited for frequency discrimination in the low-frequency range. The accessible temporal span is limited—empirically quantified as τ ≈ 35 ms for low-frequency phase-changing tones—by both neural phase-locking capacity and auditory integration (Reichenbach et al., 2012).

Interaural Coherence and Monaural Filtering: In binaural detection, temporal span is determined by the auditory filter bandwidth. The inverse relationship τ_c ≈ 1/B, where B is the effective bandwidth (e.g., ERB ≈ 79 Hz), predicts a temporal window of ≈10–15 ms for typical human listeners at 500 Hz; narrower-band noise extends this window (e.g., to 40 ms for 25 Hz bandwidth) (Dietz et al., 2021).

Forward Masking and Slow Inhibition: In auditory streaming, the effective temporal window for perceptual interaction between sequential tones is determined by the onset delay and decay time constant of inhibitory synaptic processes (D, τ_i). Forward masking persists for approximately D + τ_i ≈ 50–100 ms, delimiting the span over which tones mask or group with subsequent tones (Ferrario et al., 2020).

Prosodic and Suprasegmental Grouping: Oscillator models and hierarchical time-tree analyses describe prosodic grouping over domains ranging from subsyllabic segments (<50 ms) up to entire utterances (several seconds), with tone-bearing syllable intervals clustering at 150–300 ms (Gibbon, 2018).

4. Language and Task Dependence in Machine Representations

Self-supervised learning (SSL) speech models internalize the temporal span of tone cues with strong dependence on language characteristics and task objectives. For instance, linear probe and gradient-based analyses on fine-tuned models show that optimal tone discrimination aligns with ~100 ms windows for Burmese and Thai, and ~180 ms for Lao and Vietnamese (Kim et al., 15 Nov 2025).

Downstream task choice modulates temporal focus:

• Automatic speech recognition (ASR) fine-tuning on target tonal languages yields temporal spans matching acoustic baselines within ±15 ms.
• Cross-lingual ASR (e.g., Mandarin as source) partially transfers tone sensitivity but with degraded temporal alignment.
• Non-ASR objectives, such as emotion or speaker recognition, broaden the temporal span (220–270 ms), reflecting sensitivity to prosodic properties rather than fine-grained tone categories. Probing performance on tone tasks deteriorates under these broader spans.

These findings specify that the span over which neural speech representations are maximally tone-sensitive is not an immutable property of the model or language but is strongly shaped by the training paradigm and target task (Kim et al., 15 Nov 2025).

5. Multi-scale Structure and Implications for Prosodic Modeling

Tone cues are nested within a hierarchy of temporal domains:

• Subsyllabic (segmental) cues: <50 ms, crucial for articulatory and low-level F0 excursions.
• Syllable-level: 150–300 ms, aligning with AEMS peaks at 4–5 Hz and tone-bearing unit durations.
• Foot-level: 400–800 ms (1.5–2.5 Hz), capturing rhythmic alternation.
• Phrase and discourse: 1–10 s (0.3–1 Hz and below), structuring intonation and rhetorical grouping (Gibbon, 2018).

This multi-scale view underlines the need for models and analyses that accommodate integration of tone cues over both short (tens to hundreds of ms) and long (seconds) spans, a requirement reflected in both human perception and computational speech technology.

6. Summary Table of Characteristic Temporal Spans

Context / Method	Characteristic Span (ms)	Reference
Auditory fine structure (phase lock)	~35	(Reichenbach et al., 2012)
Monaural filtering / binaural cues	~10–15	(Dietz et al., 2021)
Forward masking in streaming	50–100	(Ferrario et al., 2020)
Lexical tone (Burmese/Thai)	100	(Kim et al., 15 Nov 2025)
Lexical tone (Lao/Vietnamese)	180	(Kim et al., 15 Nov 2025)
Syllable/prosodic rhythm (AEMS)	150–300	(Gibbon, 2018)

These empirically grounded integration windows provide constraints on auditory modeling, neural coding theories, and the design/evaluation of computational systems for tonal language processing. Converging evidence from psychoacoustics, neurobiology, signal processing, and machine learning underscores the cross-disciplinary relevance of the temporal span of tone cues.