Phonological Interference in Multilingual Speech Models

Moran Yanuka; Raja Giryes; Moris Alper

cs.CLOct 8, 2026

Phonological Interference in Multilingual Speech Models

Authors: Moran Yanuka, Raja Giryes, Moris Alper

Organizations: Tel Aviv University · University of Miami

Abstract

Phoneme-level models transcribe or generate speech as a sequence of phonemes, the smallest sound units that distinguish words. These models enable fine-grained pronunciation control and understanding, yet often fail on input that does not match any single training language, such as speech alternating between two languages, known as code-switching, or low-resource languages absent from training. We identify a systematic failure mode behind this, phonological interference: models assume the input is in a single language and impose its phonology, overriding local phoneme-level decisions that conflict with the assumed language. We measure interference by how often a model retains phonemes that one language has but the other lacks. On code-switched input, two phone recognizers (speech-to-phoneme models) and a phoneme-conditioned text-to-speech model lose 32% to 79% of these phonemes, but lose far fewer of the phonemes both languages share. On unseen languages, we find that phone recognizers impose the phonology of the training language they assign to the speech, and the more confident the assignment, the more they lose phonemes the unseen language has but the assigned language lacks. We probe the models' language estimate from their internal activations, and trace interference to a low dimensional subspace. On monolingual speech, steering this subspace toward another language makes the model lose the phonemes that only the original language uses and produce phonemes that only the target language has. We introduce windowed language estimation (WLE), an inference time repair that replaces the model's language estimate in this subspace with one computed from a short window around each position. On code-switched input, WLE removes 34% to 69% of the interference in all three models, and in the recognizers it leaves monolingual performance essentially unchanged.

Figures & tables

Panel of Figure 1 — Figure 1: Phonological interference. Multilingual phoneme-level speech models estimate the language from the whole input and override individual phoneme predictions. (a) In a French–English code-switched utterance, a phone recognizer replaces English [⁢r] in right with French [K]. The bar shows the model’s language estimate in a low dimensional language subspace at each position, with time running left to right. The model computes this estimate from the whole utterance, so it blends French (orange) into English (blue), and the model still reads the English span partly as French. (b) Windowed language estimation (WLE) replaces the language subspace coordinates at each position with those the model computes from a short window of audio around it. The estimate is then computed from the local language context, and [⁢r] is correctly transcribed.

Figure 2: Qualitative examples of interference. PhoneticXeus transcriptions aligned to the reference, with unshared phonemes in bold. Output at unshared phonemes is shaded blue when the phoneme is kept, orange with a frame and bold type when it is replaced by the other language’s phoneme, and grey for any other error (

\varnothing

marks a deletion). sep transcribes the second span without the other language in its context, and tog transcribes it with the other language in its context. In the French–English example, English “the score” takes French [d] and [K]. The unseen language rows (Section 2.4 ) have no sep / tog conditions, so we list the model-assigned language instead.

Figure 3: Phonological interference on code-switched input. Each bar runs from recall with each span processed separately ( sep , hollow) to recall with both languages together ( tog , filled); its length is the interference

\Delta

. Red: unshared phonemes. Grey: shared phonemes. Unshared phoneme recall drops much more than shared phoneme recall in every model and dataset (Table 5 ).

Figure 4: Phonological interference in unseen languages. Recall on 43 DoReCo languages, split by the model’s language probe confidence: lowest third (hollow) vs. highest third (filled). Unshared phoneme recall (red) drops the more the model is confident in the estimated language, while shared phonemes (grey) remain stable, isolating interference from general difficulty of unseen languages.

Panel of Figure 5 — Figure 5: The language estimate drops on code-switched input, and editing it reproduces interference. (a) Probe score on each span’s own language, processed separately ( sep , hollow) vs. together ( tog , filled), with the conditions of Section 2.2 . (b) Recall on monolingual utterances before (hollow) and after (filled) the language subspace is set to the mean of another language $B$ . Red: unshared phonemes. Grey: shared phonemes. $\Delta$ is the drop in each panel.

Model	Condition	$r_{\min}^{\textsc{tog}}\uparrow$	$\Delta$ (interference) $\downarrow$
TTS	No intervention	0.410	0.302
	WLE, fixed window	0.513	0.199
	WLE , span window	0.619	0.093
	Oracle	0.606	0.106
PhoneticXeus	No intervention	0.138	0.507
	WLE	0.430	0.214

Table 1: Effect of WLE on interference. Best result in bold . WLE removes 69% of the interference in TTS with the span window and 34% with the fixed window, 58% in PhoneticXeus, and 36% in POWSM. The Oracle rows (gray) use the true language of every frame or span and serve as a reference (Appendix J ). POWSM rows use its CTC head.

r_{\min}^{\textsc{tog}}

and

\Delta

as in Section 2.2 .

	Model	Benchmark	$\Delta$ PER (pp) $\downarrow$		$\Delta$ PFER (pp) $\downarrow$
			Sliding window	WLE	Sliding window	WLE
Seen	POWSM	TIMIT	$+0.96$	$\mathbf{+0.07}$	$+0.47$	$\mathbf{+0.01}$
		L2-ARCTIC	$+1.55$	$\mathbf{+0.10}$	$+0.31$	$\mathbf{-0.00}$
		GMU accent	$+2.14$	$\mathbf{-0.32}$	$+0.73$	$\mathbf{-0.07}$
	PhoneticXeus	TIMIT	$+1.33$	$\mathbf{+0.08}$	$+0.49$	$\mathbf{+0.03}$
		L2-ARCTIC	$+3.22$	$\mathbf{+0.14}$	$+0.50$	$\mathbf{+0.03}$

Table 2: Performance in monolingual speech. Change in phone error rate (

\Delta

PER) and phone feature error rate (

\Delta

PFER), in percentage points, on monolingual benchmarks relative to unmodified decoding. Negative = improvement. The lower (better) value in each pair is in bold . The sliding window degrades PER and PFER much more than WLE, except for POWSM’s PFER on DoReCo.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Language pair	Replacement			Example	Language pair	Replacement			Example
English–French		$\rightarrow$	K	r eally	German–Spanish	I	$\rightarrow$	i	m i t
English–German	w	$\rightarrow$	v	w as	English–German	D	$\rightarrow$	d	th e
English–Spanish	A	$\rightarrow$	o	pr o bably	English–Spanish	z	$\rightarrow$	s	i s
German–Hindi	K	$\rightarrow$	R	de r	English–Italian	æ	$\rightarrow$	a	a nd

Appendix

Table 3: Examples of unshared phonemes from language pairs in our experiments. Each phoneme at the left of an arrow occurs only in the first language of the pair; the arrow shows its most common replacement when both languages are processed together, with an example word whose bold letters spell the phoneme. Table 4 lists the full sets for every pair and dataset.

SwitchLingua
Pair (A–B)	Only in A	Only in B
English–French	⁢r 54 D 33 30 æ 29 2 21 h 18	K 141 y 45 œ 11 ø 4
English–German	⁢r 53 w 28 æ 28 D 28 18 T 8 Z 6	K 124 ç 44 5 35 ø 13 x 11 y 10 œ 5 Y 5
English–Hindi	f 35 D 33 æ 31 27 2 24 w 21 A 17 v 15 T 7	R 61 V 19 k h 18 b h 12 t h 11 S h 9 p h 8 t 7 d 6 Z h 3
English–Italian	I 105 ⁢r 54 @ 38 U 30 2 27 D 18 N 15 æ 11 A 11 11 T 10 h 6	r 173 L 5
English–Korean	@ 63 ⁢r 62 U 38 35 f 30 D 27 æ 26 v 17 O 14 A 12 Z 5 T 3	W 119 R 77 57 C 53 47

Appendix

Table 4: The full unshared phoneme sets of every language pair in both code-switched datasets. Since selected phonemes depend on an occurrence threshold in observed data, the exact phonemes selected may differ between the datasets for the same pair of languages (e.g., comparing English–German in both datasets). Phonemes are ordered from most to least frequent, and the subscript is the number of occurrences in those reference spans.

Model	$\Delta$	$\Delta/r^{\textsc{sep}}$	$\Delta_{\text{exact}}$	$\Delta$	$\Delta/r^{\textsc{sep}}$	Ratio
	Unshared phonemes			Shared phonemes
TTS (CosyVoice)	.302 [.265, .340]	.424	.224 [.186, .262]	.077 [.065, .090]	.105	2.9 $\times$
Phone rec. (PhoneticXeus)	.507 [.462, .551]	.786	.345 [.304, .387]	.167 [.145, .188]	.244	2.1 $\times$
Phone rec. (POWSM, CTC)	.448 [.404, .490]	.754	.365 [.327, .402]	.136 [.110, .161]	.210	2.7 $\times$
Phone rec. (POWSM, attn.)	.414 [.371, .456]	.769	.268 [.233, .304]	.139 [.116, .164]	.202	1.9 $\times$

Appendix

Table 5: Phonological interference on code-switched input (full numbers behind Figure 3), with 95% bootstrap intervals.

\Delta

: drop in worst span recall from sep to tog .

\Delta/r^{\textsc{sep}}_{\min}

: drop as a fraction of the sep level.

\Delta_{\text{exact}}

: the same drop under exact match, the rule used for shared phonemes; unshared

\Delta

also accepts substitutions by phonemes the other language lacks. Ratio: unshared

\Delta_{\text{exact}}

divided by shared

\Delta

	$r_{\min}$			$\Delta$ from
Model	Alone	Same lang.	Other lang.	Alone	Same lang.
TTS (CosyVoice)	.570	.571	.318	.252 [.217, .287]	.253 [.222, .284]
Phone rec. (PhoneticXeus)	.591	.582	.396	.196 [.165, .228]	.186 [.156, .218]
Phone rec. (POWSM, CTC)	.636	.639	.180	.456 [.421, .491]	.459 [.425, .494]
Phone rec. (POWSM, attn.)	.598	.610	.165	.433 [.394, .472]	.445 [.408, .482]

Appendix

Table 6: Two sep baselines on Spliced FLEURS. Worst span recall of unshared phonemes with each half alone, next to a same language half ( sep in the main text) and next to the other language half ( tog ), and the drop to tog from each baseline, with 95% bootstrap intervals.

Setting	Model	$\gamma$ (log-odds)	Odds ratio $e^{\gamma}$
Spliced FLEURS	POWSM, CTC head	0.77 [0.61, 0.97]	2.2 [1.8, 2.6]
	POWSM, attn. decoder	0.84 [0.66, 1.04]	2.3 [1.9, 2.8]
	PhoneticXeus	0.27 [0.10, 0.47]	1.3 [1.1, 1.6]
	TTS (CosyVoice)	0.10 [ $-$ 0.02, 0.22]	1.1 [0.98, 1.3]
Edit toward $B$	POWSM, CTC head	2.28 [2.07, 2.53]	9.8 [7.9, 12.6]
	PhoneticXeus	2.44 [2.31, 2.64]	11.5 [10.1, 14.1]

Appendix

Table 7: The same phoneme next to languages that lack it and languages that have it.

\gamma

: additional drop in the log-odds of recalling a phoneme when the other language lacks it, with the phoneme and the language pair held fixed; 0 if a phoneme’s difficulty alone decided the drop. 95% bootstrap intervals. The TTS interval on Spliced FLEURS includes 0.

Dataset	Model	Added substitutions	Most similar phoneme	Share
Spliced FLEURS	POWSM, CTC head	2.64 [2.42, 2.87]	0.68	0.29
	POWSM, attn. decoder	2.86 [2.61, 3.10]	0.68	0.25
	PhoneticXeus	1.78 [1.46, 2.11]	0.59	0.42
	TTS (CosyVoice)	2.47 [2.18, 2.75]	0.79	0.35
SwitchLingua	POWSM, attn. decoder	3.36 [3.03, 3.72]	0.77	0.19
	PhoneticXeus	2.76 [2.45, 3.10]	0.70	0.29

Appendix

Table 8: Replacements are far from the phonemes they replace. Added substitutions: mean PanPhon feature distance between an unshared phoneme and the other language’s phoneme that replaces it, over the replacements the other language adds (95% bootstrap intervals). Most similar phoneme: the same mean if each had been replaced by the closest phoneme of the other language. Share: fraction of replacements that are that closest phoneme.

Figure 6: Language probe accuracy by layer , on held-out monolingual speech. Held-out speakers : 5,800 utterances from 1,112 unseen speakers. Held-out speakers and corpus : each point is from a probe trained without that language’s corpus, so neither the speakers nor the recording pipeline have been seen. The vertical line marks the first layer within 0.01 of the best. For the TTS, the probe reads the language model at each speech token position, with the utterance’s own speech tokens as input. We read language estimates at layer 8 (POWSM), layer 17 (PhoneticXeus) and block 7 of the TTS language model.

Figure 7: Subspace edit sweeps. Left: effect of varying the start block (rank 15, development speakers). Middle: effect of varying rank (at the selected start block, test speakers). Right: probe accuracy for the true language inside the rank-

k

subspace (chance dotted). Solid: cumulative edit from start block to last; dashed: single block only. Blue: unwhitened; orange: whitened. See legend for additional markers.

	$\Delta_{\text{unshared}}$	$\Delta_{\text{shared}}$	$\Delta_{\text{sh}}/\Delta_{\text{unsh}}$
Language subspace $\to B$	.777 [.757, .791]	.229 [.220, .236]	.29
Language subspace $\to C$ (against $C$ )	.777 [.756, .792]	—	—
One direction ( $A\negthinspace\to\negthinspace B$ )	.753 [.730, .767]	.228 [.220, .235]	.30
Erased to global mean	.417 [.399, .429]	.109 [.103, .114]	.26
Random shift (seed 1)	.029 [.025, .032]	.006 [.004, .009]	—
Random shift (seed 2)	.030 [.025, .035]	.006 [.005, .008]	—

Appendix

Table 9: Manipulating the language subspace reproduces interference (full numbers behind Figure 5(b) ).

\Delta

: drop in recall from unedited to edited output, paired per utterance.

\Delta_{\text{sh}}/\Delta_{\text{unsh}}

: selectivity ratio; lower means the edit targets unshared phonemes more precisely. CIs are 95% bootstrap over speaker groups within language. The

\to C

row gives the drop in recall of

A

’s phonemes unshared with

C

; the other two columns are defined against

B

and are left empty for it, and for the TTS model it uses 1,515 utterances with one decode each. Edited transcripts retain 96–98% of unedited length in all conditions.

	SwitchLingua	Spliced FLEURS
WER, sep	.160	.152
WER, tog	.259	.540
$\Delta$ WER	$+.099$ [.071, .126]	$+.388$ [.356, .423]
$\Delta$ WER, span in the picked language	$-.010$ [ $-$ .027, .006]	$+.045$ [.007, .097]
$\Delta$ WER, the other span	$+.223$ [.168, .282]	$+.741$ [.695, .784]

Appendix

Table 10: Whisper loses the span that is not in the language it picks. WER rises when the other language is present. The span in the picked language is transcribed about as well as it is alone, and almost all of the rise comes from the other span, which Whisper translates or drops. WER is the number of word errors divided by the number of reference words, over both spans.

\Delta

is tog minus sep on the same recordings, with 95% bootstrap intervals over recordings. As in Section 2.2 , sep is the span alone on SwitchLingua, and the span next to another speaker of the same language on Spliced FLEURS. The bottom rows use the 274 recordings and 286 clips where Whisper picks one of the two languages.

Edit	$\Delta P(B)$	$\Delta$ transcript in $B$	$\Delta P(A)$
Toward $B$	$+.201$ [.184, .212]	$+.221$ [.195, .238]	$-.447$ [ $-$ .465, $-$ .427]
Toward $C$	$+.007$ [.005, .008]	$+.002$ [.000, .003]	$-.449$ [ $-$ .468, $-$ .429]
Mean of all centroids	$+.008$ [.006, .010]	$+.003$ [.001, .005]	$-.276$ [ $-$ .298, $-$ .261]
Random subspace	$+.001$ [.000, .001]	$+.000$ [.000, .000]	$-.013$ [ $-$ .019, $-$ .010]

Appendix

Table 11: Whisper transcribes in the language its encoder is steered toward. Every edit of the language subspace lowers the probability of

A

, and only the edit toward

B

raises the probability of

B

and moves the transcript to

B

A

is the language of the utterance,

B

is the target language and

C

is a third language.

P(A)

and

P(B)

are the probabilities Whisper gives to the language tokens of

A

and

B

. “Transcript in

B

” is the text language identifier’s confidence that the transcript is written in

B

. Each entry is the change from the unedited model, where

P(A)

0.963

. Entries are averaged within each source language and then over the 14 languages, with 95% bootstrap intervals over speaker groups. The edit toward

C

uses the 2,027 utterances whose

C

has a language token.

Model	Condition	$r_{\min}^{\textsc{tog}}\uparrow$	$\Delta$ (interference) $\downarrow$
TTS	No intervention	.410	.302 [.265, .340]
	In chunks, no edit	.351	.361 [.328, .394]
	wle , fixed window	.513	.199 [.175, .223]
	wle , fixed window, random subspace	.332	.380 [.350, .411]
	Span by span, no edit	.383	.329 [.291, .368]
	wle , span window	.619	.093 [.068, .120]

Appendix

Table 12: Interference on SwitchLingua with 95% bootstrap intervals over recordings. In all three models the random subspace leaves interference at the level of the same decoding without the edit.

$\Delta$ PER (pp)
Model	Benchmark	Sliding window	wle	wle , random subspace
POWSM	TIMIT	$+0.96$ [ $+0.85$ , $+1.07$ ]	$+0.07$ [ $+0.02$ , $+0.11$ ]	$+0.01$ [ $-0.02$ , $+0.04$ ]
	L2-ARCTIC	$+1.55$ [ $+1.37$ , $+1.74$ ]	$+0.10$ [ $+0.01$ , $+0.19$ ]	$+0.02$ [ $-0.02$ , $+0.07$ ]
	GMU accent	$+2.14$ [ $+2.00$ , $+2.29$ ]	$-0.32$ [ $-0.39$ , $-0.26$ ]	$-0.04$ [ $-0.07$ , $-0.01$ ]
	DoReCo	$+1.97$ [ $+1.77$ , $+2.19$ ]	$-0.09$ [ $-0.19$ , $+0.02$ ]	$-0.13$ [ $-0.18$ , $-0.09$ ]
PhoneticXeus	TIMIT	$+1.33$ [ $+1.22$ , $+1.44$ ]	$+0.08$ [ $+0.05$ , $+0.11$ ]	$+0.01$ [ $-0.01$ , $+0.03$ ]

Appendix

Table 13:

\Delta

PER (top) and

\Delta

PFER (bottom), in percentage points, behind Table 2 , with 95% bootstrap intervals over clips.

Model	Word	Reference	sep	tog	WLE
(a) English words in a French–English sentence: Le Président américain a annoncé une décision importante aujourd’hui. Can you believe he revoked Biden’s security clearance?
TTS	revoked	IvoUkt	ivoUkt	K ivOk	voUkt
	security	sIkjUIti	sIkjUIdi	sikju K iti	sIkjUIdi
PhoneticXeus	revoked	IvoUkt	IvoUkt	K ivOkt	ivoUkt
	security	sIkjUIti	sIkjU@ti	sk yK iti	skjUiti
POWSM	revoked	IvoUkt	IvoUktu	K @vOkt	@vokt

Appendix

Table 14: Examples of WLE. Each model’s transcript of the words in bold when it processes each span alone ( sep ), the whole utterance ( tog ), and the whole utterance with WLE. For the TTS model, WLE uses the span window, and the transcript is PhoneticXeus’s reading of the synthesized span (first sampling seed). In the reference, the unshared phonemes of the word’s own language are in bold. In the transcripts, phonemes that only the other language uses are in red .

PhoneticXeus, SwitchLingua (no intervention: .507)
Condition	1 s	2 s	3 s	4 s
Sliding window	.223 [.188, .260]	.202 [.160, .243]	.207 [.163, .250]	.246 [.203, .290]
wle	.185 [.146, .222]	.214 [.171, .257]	.259 [.216, .304]	.306 [.263, .349]
POWSM, SwitchLingua (no intervention: .448)
Sliding window	.245 [.203, .287]	.153 [.120, .187]	.184 [.151, .218]	.211 [.171, .251]
wle	.321 [.282, .359]	.288 [.249, .327]	.294 [.255, .333]	.330 [.289, .371]

Appendix

Table 15: Interference

\Delta

(

\downarrow

) by window width, with 95% bootstrap intervals over recordings or clips. The unrepaired

\Delta

is in parentheses, and the lowest value in each row is shown in bold .

Share of the window’s reduction
	SwitchLingua		Spliced FLEURS
Basis	PhoneticXeus	POWSM	PhoneticXeus	POWSM
No repair, $\Delta$	.507	.448	.186	.459
Sliding window (2 s), $\Delta$	.202	.153	.037	.081
All 16 languages, rank 15 ( wle )	.96	.54	.93	.60
7 code-switched languages, rank 6	.88	.52	.92	.55

Appendix

Table 16: Share of the sliding window’s interference reduction recovered by wle with each basis. Each value is

(\Delta_{\text{no repair}}-\Delta_{\textsc{wle}})\,/\,(\Delta_{\text{no repair}}-\Delta_{\text{window}})

. The full 16-language basis recovers most of the window’s effect. Bases missing one language of the evaluated pair show partial repair; on SwitchLingua, whose pairs all contain English, bases missing both languages recover a quarter to a third of the window’s reduction in PhoneticXeus and no more than a random basis in POWSM.

	SwitchLingua		Spliced FLEURS
Condition	PhoneticXeus	POWSM	PhoneticXeus	POWSM
No intervention	.507 [.462, .551]	.448 [.404, .490]	.186 [.156, .218]	.459 [.425, .494]
Sliding window (2 s)	.202 [.160, .243]	.153 [.120, .187]	.037 [.016, .059]	.081 [.060, .102]
wle	.214 [.171, .257]	.288 [.249, .327]	.047 [.023, .073]	.234 [.205, .264]
wle , random subspace	.494 [.449, .539]	.451 [.407, .494]	.178 [.148, .211]	.440 [.406, .475]
Oracle	.108 [.073, .144]	.121 [.080, .162]	.028 [.005, .050]	.143 [.118, .169]

Appendix

Table 17: Interference

\Delta

(lower is better) with 95% intervals. The oracle reduces interference beyond wle on all four cells, and beyond the sliding window on three. The same edits through a random subspace have no effect, confirming the reduction depends on the language subspace.

Language tag	SwitchLingua	Spliced FLEURS
<unk>	.414 [.371, .456]	.445 [.408, .482]
Each span’s own language	.102 [.070, .134]	.248 [.213, .284]
Difference	.312 [.263, .360]	.197 [.160, .235]

Appendix

Table 18: POWSM’s attention decoder with each span’s own language tag. Interference

\Delta

(lower is better), the drop in worst span recall of unshared phonemes from sep to tog , with 95% bootstrap intervals, on the recordings (266) and clips (274) that both decodes can score. Difference:

\Delta

under <unk> minus

\Delta

under the span’s own tag, paired per recording or clip.

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Language Discrimination Improves Linguistic Learning in Multilingual Speech Models

Maureen de Seyssel, Jie Chi, Zakaria Aldeneh

Apple

Self-Supervised Speech Representation Learning

Open paper AskPDF arXiv

Multilingual self-supervised speech models can benefit from sharing information across languages, but under a matched total pretraining data budget they still fall short of monolingual models. We show that strengthening the model's ability to discriminate languages during pretraining reduces and, on some measures, closes this multilingual gap on continuous phonetic and higher-level linguistic measures, while preserving substantial cross-language sharing. Using a controlled English/French HuBERT setting, we test two interventions which strengthen language discrimination: an auxiliary language classifier and per-language k-means targets. Across interventions, continuous-feature phone discrimination error (phone-ABX, lower is better) decreases from 11.6% in the bilingual baseline to 10.4% (monolingual: 10.8%), while lexical performance (sWUGGY, higher is better) increases from 52.1% to 56.7% (monolingual: 58.5%) and prosodic performance (ProsAudit, lexical subtask, higher is better) from 68.9% to 72.9% (monolingual: 72.6%). Across HuBERT training stages, the strongest gains on most linguistic measures occur when language discrimination is introduced in the first iteration, whereas later or repeated interventions yield smaller improvements and are accompanied by increased language-wise segregation. These results support a causal role for language discrimination in reducing the additional cost of multilingual learning.

Abstract

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